def slide_phantom(shot, camera='phantom2', sub=20, blur=3, interval=50,
pixel_t_hist=None):
"""
Slide through Phantom frames while displaying last closed flux surface and
relevant timeseries plots.
Parameters
shot: int, shot number
camera: string e.g. 'phantom' (outboard midplane GPI),
'phantom2' (X-point GPI)
sub: number of frames to use in average subtraction. 20 works well.
blur: extent of Gaussian blur, 1, 2, or 3 advisable
interval: delay in ms between frames during play
pixel_t_hist: (x,y) to show time history of that pixel instead of
H-alpha.
"""
time = acquire.gpi_series(shot, camera, 'time')
frames = acquire.video(shot, camera)
frame_count = frames.shape[0]
# Plot GPI and LCFS
gs = gridspec.GridSpec(3, 1, height_ratios=[3, 1, 1])
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25, hspace=.43, top=.96)
plt.subplot(gs[0])
im = plt.imshow(frames[0], origin='lower', cmap=plt.cm.gist_heat)
plt.colorbar()
plt.axis('off')
# Plot H_alpha or pixel timeseries
plt.subplot(gs[1]).locator_params(axis='y', nbins=4)
if pixel_t_hist:
plt.title('Pixel time history')
plt.plot(time,
frames[:, pixel_t_hist[0], pixel_t_hist[1]].swapaxes(0, 1))
else:
plt.title('H-alpha')
time_ha2, ha2 = process.time_crop(acquire.time_ha2(shot), time)
plt.plot(time_ha2, ha2)
vl1 = plt.axvline(time[0], color='r')
plt.xlim([time[0], time[-1]])
# Plot line average density
time_dens, dens = process.time_crop(acquire.time_dens(shot), time)
plt.subplot(gs[2]).locator_params(axis='y', nbins=4)
plt.title('Line Average Density')
plt.plot(time_dens, dens)
vl2 = plt.axvline(time[0], color='r')
plt.xlabel('Time (s)')
plt.xlim([time[0], time[-1]])
curr_frame = 0
def update(val):
global frames, curr_frame
val = int(val)
curr_frame = val
curr_time = time[val]
slider.valtext.set_text('%d (t=%.5f s)' % (val, curr_time))
if val < frame_count:
im.set_array(frames[val])
vl1.set_xdata(curr_time)
vl2.set_xdata(curr_time)
fig.canvas.draw_idle()
# Slider settings
slide_area = plt.axes([0.10, 0.1, 0.65, 0.03])
slider = Slider(slide_area, 'Frame', 0, frame_count-1, valinit=0)
slider.drawon = True
slider.valfmt = '%d'
slider.on_changed(update)
def init():
global curr_frame
curr_frame = int(curr_frame)
start_frame = curr_frame
im.set_data(frames[curr_frame])
vl1.set_xdata(time[curr_frame])
vl2.set_xdata(time[curr_frame])
for i in xrange(curr_frame, curr_frame + 200, 1):
slider.set_val(i)
vl1.set_xdata(time[i])
vl2.set_xdata(time[i])
slider.set_val(start_frame)
return [im, vl1, vl2]
def animate(i):
im.set_array(frames[i])
vl1.set_xdata(time[i])
vl2.set_xdata(time[i])
return [im, vl1, vl2]
def play(event):
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=100, interval=interval,
blit=False)
def forward(event):
global curr_frame
slider.set_val(curr_frame + 1)
def backward(event):
global curr_frame
slider.set_val(curr_frame - 1)
# Time button settings
play_button_area = plt.axes([0.45, 0.025, 0.1, 0.04])
play_button = Button(play_button_area, 'Play')
play_button.on_clicked(play)
forward_button_area = plt.axes([0.56, 0.05, 0.04, 0.025])
forward_button = Button(forward_button_area, '>')
forward_button.on_clicked(forward)
back_button_area = plt.axes([0.56, 0.015, 0.04, 0.025])
back_button = Button(back_button_area, '<')
back_button.on_clicked(backward)
def apply_filter(label):
global frames
if label == 'Orig':
frames = acquire.video(shot, camera)
elif label == 'Sub %d' % sub:
frames = acquire.video(shot, camera, sub=sub)
elif label == 'Blur %d' % blur:
frames = acquire.video(shot, camera, sub=sub, blur=blur)
elif label == 'Sobel':
frames = acquire.video(shot, camera, sub=sub, blur=blur,
sobel=True)
update(curr_frame)
im.autoscale()
def recolor(event):
im.autoscale()
def cmap_change(label):
global curr_frame
if label == 'Red': im.cmap = plt.cm.gist_heat
if label == 'Gray': im.cmap = plt.cm.gray
update(curr_frame)
im.autoscale()
# Image button settings
left = .3
bottom = .79
filter_radio_area = plt.axes([left, bottom, .1, .12])
filter_radio = RadioButtons(filter_radio_area, ('Orig', 'Sub %d' % sub,
'Blur %d' % blur,
'Sobel'))
filter_radio.on_clicked(apply_filter)
cmap_radio_area = plt.axes([left, bottom-.08, .1, .07])
cmap_radio = RadioButtons(cmap_radio_area, ('Red', 'Gray'))
cmap_radio.on_clicked(cmap_change)
recolor_button_area = plt.axes([left, bottom-.13, 0.1, 0.04])
recolor_button = Button(recolor_button_area, 'Recolor')
recolor_button.on_clicked(recolor)
# Black magic to fix strange global variable error
apply_filter('Orig')
plt.show()
slbutton = Button(ax9, 'RWD <<', color='magenta', hovercolor='0.975') slbutton.on_clicked(sl_onClick) ax10 = plt.axes([0.47+0.2, 0.76, 0.05, 0.04]) readybutton = Button(ax10, 'Set label \nstart time', color='yellow', hovercolor='0.975') readybutton.on_clicked(set_label_start) ax11 = plt.axes([0.47+0.2, 0.71, 0.05, 0.04]) logbutton = Button(ax11, 'Stop time \nLog label', color='green', hovercolor='0.975') logbutton.on_clicked(log_label) ax12 = plt.axes([0.87, 0.81, 0.05, 0.09]) savebutton = Button(ax12, 'Save labels\nto file', color='cyan', hovercolor='0.975') savebutton.on_clicked(save_label) startax = plt.axes([0.15, 0.625, 0.25, 0.03]) stopax = plt.axes([0.55, 0.625, 0.25, 0.03]) start = Slider(startax, 'Label Start \nTimestamp', data.index[0].value//10**3, data.index[-1].value//10**3, valinit=data.index[0].value//10**3) stop = Slider(stopax, 'Label Stop\nTimestamp', data.index[0].value//10**3, data.index[-1].value//10**3, valinit=data.index[0].value//10**3) start.valfmt = '%i' stop.valfmt = '%i' rangeax = plt.axes([0.47+0.2, 0.67, 0.18, 0.03]) drange = Slider(rangeax,'Constant\ntimedelta(s)', -100, 100, valinit=0) drange.valfmt = '%i' rax = plt.axes([0.53+0.2, 0.71, 0.06, 0.19]) radio = RadioButtons(rax, labels, active=0) #radio = CheckButtons(rax, labels[1:], [False]*len(labels[1:])) rax1 = plt.axes([0.60+0.2, 0.71, 0.06, 0.19]) radio1 = RadioButtons(rax1, ['NA']*(num_class+1), active=0) #radio1 = CheckButtons(rax1, range(num_class), [False]*num_class) radio.on_clicked(r_label) radio1.on_clicked(r1_label) def itergen(): global press_delta i_max = data.shape[0]
def slide_corr(frames, pixel, other_pixels=None):
"""
Display correlation values for a pixel with a slider for lag time.
Parameters
frames: NumPy array of correlations with dimension (no. lags, x pixels,
y pixels)
pixel: (x, y) to mark with a circle
other_pixels: array of (x, y) values of pixels to mark
"""
frame_count = frames.shape[0]
# Initial plotting
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.subplots_adjust(bottom=0.25)
im = plt.imshow(frames[frame_count/2], origin='bottom',
cmap=plt.cm.RdBu_r, vmin=-1, vmax=1)
circ = plt.Circle(pixel[::-1], radius=1, edgecolor='r', fill=False)
ax.add_patch(circ)
if other_pixels:
t_hists_r = [o[0] for o in other_pixels]
t_hists_z = [o[1] for o in other_pixels]
else:
t_hists_r = acquire.gpi_series(1150611004, 'phantom2', 'hist_xpix')
t_hists_z = acquire.gpi_series(1150611004, 'phantom2', 'hist_ypix')
for pos in zip(t_hists_r, t_hists_z):
ax.add_patch(plt.Circle(pos, radius=1, edgecolor='b', fill=False))
plt.colorbar()
plt.title('Correlation for pixel (%d, %d)' % pixel)
plt.xlabel('Pixel y coordinate')
plt.ylabel('Pixel x coordinate')
# Slider and button settings
slide_area = plt.axes([0.10, 0.1, 0.65, 0.03])
slider = Slider(slide_area, 'Lag', -frame_count/2, frame_count/2, valinit=0)
slider.drawon = True
slider.valfmt = '%d'
play_button_area = plt.axes([0.45, 0.025, 0.1, 0.04])
play_button = Button(play_button_area, 'Play')
curr_frame = 0
def update(val):
global curr_frame
curr_frame = val + frame_count/2
slider.valtext.set_text('%d' % val)
if curr_frame < frame_count:
im.set_array(frames[curr_frame])
fig.canvas.draw_idle()
slider.on_changed(update)
def init():
global curr_frame
curr_frame = int(curr_frame)
im.set_data(frames[curr_frame])
for i in xrange(curr_frame, curr_frame + 200, 2):
slider.set_val(i)
return [im]
def animate(i):
im.set_array(frames[i])
return [im]
def play(event):
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=
frame_count, interval=0, blit=False)
play_button.on_clicked(play)
plt.show()
def slide_correlation(shot, fl_sav):
"""
Slide through Phantom camera frames using given synthetic field line images
to create a reconstruction of the original using cross-correlation to find
the best match.
Args:
shot: [int] shot number
fl_sav: [scipy.io.idl.readsav object] containing field line images
"""
time = acquire.gpi_series(shot, 'phantom2', 'time')
frames = acquire.video(shot, 'phantom2', sub=20, sobel=False)
frame_index = 0
fls = fl_sav.fl_image
fl_r = fl_sav.fieldline_r
fl_z = fl_sav.fieldline_z
rlcfs, zlcfs = acquire.lcfs_rz(shot)
efit_times, flux, flux_extent = acquire.time_flux_extent(shot)
efit_t_index = process.find_nearest(efit_times, time[0], ordered=True)
machine_x, machine_y = acquire.machine_cross_section()
# Find cross-correlation scores between frames and field line images
xcorrs = np.zeros(fls.shape[0])
for i, fl in enumerate(fls):
xcorrs[i] = signals.cross_correlation(frames[frame_index], fl)
indices = np.argsort(xcorrs)
# Interpolate field line cross-correlation scores over R, Z grid
r_space = np.linspace(min(fl_r), max(fl_r), 100)
z_space = np.linspace(min(fl_z), max(fl_z), 100)
r_grid, z_grid = np.meshgrid(r_space, z_space)
xcorr_grid = matplotlib.mlab.griddata(fl_r, fl_z, xcorrs, r_grid, z_grid,
interp='linear')
# Plot camera image with field line overlay
fig, ax = plt.subplots()
fig.suptitle('Shot {}'.format(shot))
plt.subplot(121)
plt.title('Divertor camera view')
plasma_image = plt.imshow(frames[frame_index], cmap=plt.cm.gray,
origin='bottom')
overlay_cmap = make_colormap([(1., 0., 0., 0.), (1., 0., 0., 1.)])
fl_image = plt.imshow(fls[indices[-1]], cmap=overlay_cmap, origin='bottom',
alpha=0.8)
plt.axis('off')
# Plot field line R, Z data in context of machine
plt.subplot(122)
plt.title('Toroidal cross section')
xcorr_image = plt.pcolormesh(r_grid, z_grid, xcorr_grid)
colorbar = plt.colorbar()
colorbar.set_label('Cross-correlation')
plt.plot(machine_x, machine_y, color='gray')
l, = plt.plot(rlcfs[efit_t_index], zlcfs[efit_t_index], color='fuchsia')
plt.axis('equal')
plt.xlim([.49, .62])
plt.ylim([-.50, -.33])
plt.xlabel('R (m)')
plt.ylabel('Z (m)')
f, = plt.plot(fl_r[indices[-1]], fl_z[indices[-1]], 'ro')
plt.contour(flux[efit_t_index], 100, extent=flux_extent)
# Slider and button settings
fl_slide_area = plt.axes([0.20, 0.02, 0.60, 0.03])
fl_slider = Slider(fl_slide_area, 'Correlation rank', 0, len(fls)-1,
valinit=0)
fl_slider.valfmt = '%d'
phantom_slide_area = plt.axes([0.20, 0.06, 0.60, 0.03])
phantom_slider = Slider(phantom_slide_area, 'Camera frame', 0, len(frames)-1,
valinit=0)
phantom_slider.valfmt = '%d'
forward_button_area = plt.axes([0.95, 0.06, 0.04, 0.04])
forward_button = Button(forward_button_area, '>')
back_button_area = plt.axes([0.95, 0.01, 0.04, 0.04])
back_button = Button(back_button_area, '<')
def update_data(val):
global frame_index, indices, xcorr_grid
frame_index = int(val)
for i, fl in enumerate(fls):
xcorrs[i] = signals.cross_correlation(frames[frame_index], fl)
xcorr_grid = matplotlib.mlab.griddata(fl_r, fl_z, xcorrs, r_grid,
z_grid, interp='linear')
indices = np.argsort(xcorrs)[::-1]
update_images(0)
def update_images(val):
global frame_index, indices, xcorr_grid
val = int(val)
efit_t_index = process.find_nearest(efit_times, time[frame_index], ordered=True)
plasma_image.set_array(frames[frame_index])
plasma_image.autoscale()
fl_image.set_array(fls[indices[val]])
fl_image.autoscale()
f.set_xdata(fl_r[indices[val]])
f.set_ydata(fl_z[indices[val]])
l.set_xdata(rlcfs[efit_t_index])
l.set_ydata(zlcfs[efit_t_index])
xcorr_image.set_array(xcorr_grid[:-1, :-1].ravel())
fig.canvas.draw_idle()
def forward(event):
global frame_index
frame_index += 1
update_data(frame_index)
def backward(event):
global frame_index
frame_index -= 1
update_data(frame_index)
fl_slider.on_changed(update_images)
phantom_slider.on_changed(update_data)
forward_button.on_clicked(forward)
back_button.on_clicked(backward)
plt.tight_layout(rect=(0, .1, 1, .9))
plt.show()
def slide_reconstruction(shot, smoothing_param=100, save=False):
"""
Slide through Phantom camera frames and their reconstructions from
synthetic field line images calculated externally with Julia.
Args:
shot: [int] shot number
"""
# Read cache files broken up by EFIT time segment
old_working_dir = os.getcwd()
os.chdir(os.path.dirname(os.path.abspath(__file__)))
emissivity_files = sorted(glob.glob('../cache/fl_emissivities_Xpt_{}_sp{}_*'.format(shot, smoothing_param)))
fl_data_files = sorted(glob.glob('../cache/fl_data_Xpt_{}_*'.format(shot)))
fl_image_files = sorted(glob.glob('../cache/fl_images_Xpt_{}_*'.format(shot)))
if len(emissivity_files) == 0 or len(fl_data_files) == 0 or len(fl_image_files) == 0:
raise Exception('Could not find files necessary to view reconstruction')
emissivities = [np.load(f) for f in emissivity_files]
fl_data = [scipy.io.idl.readsav(f) for f in fl_data_files]
fl_images = [np.load(f) for f in fl_image_files]
os.chdir(old_working_dir)
frame_index = 0
times = acquire.gpi_series(shot, 'phantom2', 'time')
frames = acquire.video(shot, 'phantom2', sub=20, sobel=False)
fl_rs = [f.fieldline_r for f in fl_data]
fl_zs = [f.fieldline_z for f in fl_data]
rlcfs, zlcfs = acquire.lcfs_rz(shot)
efit_times, flux, flux_extent = acquire.time_flux_extent(shot)
machine_x, machine_y = acquire.machine_cross_section()
efit_t_index = process.find_nearest(efit_times, times[0], ordered=True)
efit_phantom_start_index = efit_t_index
previous_segments_frame_count = [sum([len(e) for e in emissivities[:t_index]]) for t_index in range(len(emissivities))]
geomatrices = [np.transpose(np.array([fl.flatten() for fl in fl_image_set])) for fl_image_set in fl_images]
reconstructed = geomatrices[0].dot(emissivities[0][0])
reconstructed = reconstructed.reshape((64,64))
fl_r_all = np.concatenate(fl_rs)
fl_z_all = np.concatenate(fl_zs)
r_space = np.linspace(np.min(fl_r_all), np.max(fl_r_all), 100)
z_space = np.linspace(np.min(fl_z_all), np.max(fl_z_all), 100)
r_grid, z_grid = np.meshgrid(r_space, z_space)
emissivity_grid = make_grid(fl_rs[0], fl_zs[0], r_grid, z_grid, emissivities[0][0])
# Draw figure using first frame
fig, ax = plt.subplots()
title = plt.suptitle('Shot {} frame {}'.format(shot, 0))
plt.tight_layout(rect=(0, .1, 1, .9))
plt.subplot(221)
plt.title('Divertor camera view')
plasma_image = plt.imshow(frames[0], cmap=plt.cm.gist_heat, origin='bottom')
plt.axis('off')
#plt.colorbar()
plt.subplot(222)
plt.title('Reconstruction')
reconstruction_image = plt.imshow(reconstructed, cmap=plt.cm.gist_heat, origin='bottom')
plt.axis('off')
#plt.colorbar()
plt.subplot(223)
plt.title('Original minus reconstruction')
error_image = plt.imshow(frames[0] - reconstructed, cmap=plt.cm.gist_heat, origin='bottom')
plt.axis('off')
#plt.colorbar()
plt.subplot(224)
plt.title('Toroidal cross section')
emissivity_image = plt.pcolormesh(r_grid, z_grid, emissivity_grid, cmap=plt.cm.plasma)
#colorbar = plt.colorbar()
#colorbar.set_label('Relative emissivity')
plt.axis('equal')
plt.plot(machine_x, machine_y, color='gray')
l, = plt.plot(rlcfs[efit_phantom_start_index],
zlcfs[efit_phantom_start_index], color='orange', alpha=0.5)
plt.xlim([.49, .62])
plt.ylim([-.50, -.33])
plt.xlabel('R (m)')
plt.ylabel('Z (m)')
plt.contour(flux[efit_t_index], 300, extent=flux_extent, alpha=0.5)
plt.gca().set_axis_bgcolor('black')
if not save:
phantom_slide_area = plt.axes([0.20, 0.02, 0.60, 0.03])
phantom_slider = Slider(phantom_slide_area, 'Camera frame', 0, len(frames)-1,
valinit=0)
phantom_slider.valfmt = '%d'
cutoff_slider_area = plt.axes([0.20, 0.05, 0.60, 0.03])
cutoff_slider = Slider(cutoff_slider_area, 'Cutoff', 0, 100, valinit=0)
cutoff_slider.valfmt = '%d%%'
forward_button_area = plt.axes([0.95, 0.06, 0.04, 0.04])
forward_button = Button(forward_button_area, '>')
back_button_area = plt.axes([0.95, 0.01, 0.04, 0.04])
back_button = Button(back_button_area, '<')
def update_frame(val):
global efit_t_index, frame_index
frame_index = int(val)
cutoff = cutoff_slider.val
# Recalculate things
efit_t_index = process.find_nearest(efit_times, times[frame_index], ordered=True)
efit_rel_index = efit_t_index - efit_phantom_start_index
frame_rel_index = frame_index - previous_segments_frame_count[efit_rel_index]
emissivity_cutoff = cutoff_array(emissivities[efit_rel_index][frame_rel_index], cutoff)
emissivity_grid = make_grid(fl_rs[efit_rel_index], fl_zs[efit_rel_index], r_grid, z_grid, emissivity_cutoff)
reconstructed = geomatrices[efit_rel_index].dot(emissivity_cutoff).reshape((64,64))
# Update canvas
plasma_image.set_array(frames[frame_index])
plasma_image.autoscale()
reconstruction_image.set_array(reconstructed)
reconstruction_image.autoscale()
emissivity_image.set_array(emissivity_grid[:-1, :-1].ravel())
emissivity_image.autoscale()
error_image.set_array(frames[frame_index] - reconstructed)
error_image.autoscale()
l.set_xdata(rlcfs[efit_t_index])
l.set_ydata(zlcfs[efit_t_index])
title.set_text('Shot {} frame {}'.format(shot, frame_index))
def forward(event):
global frame_index
phantom_slider.set_val(frame_index + 1)
def backward(event):
global frame_index
phantom_slider.set_val(frame_index - 1)
def update_cutoff(val):
global frame_index
update_frame(frame_index)
if save:
FFMpegWriter = animation.writers['ffmpeg']
writer = FFMpegWriter(fps=15, bitrate=5000)
anim = animation.FuncAnimation(fig, update_frame, frames=1000, interval=5, blit=False)
anim.save('{}_sp{}.avi'.format(shot, smoothing_param), writer=writer)
plt.close(fig)
else:
phantom_slider.on_changed(update_frame)
cutoff_slider.on_changed(update_cutoff)
forward_button.on_clicked(forward)
back_button.on_clicked(backward)
plt.show()
def slide_reconstruct(shot, fl_sav, smoothing_param=100):
"""
Slide through Phantom camera frames using given synthetic field line images
to create a reconstruction of the original using non-negative least squares
(NNLS) fitting. The NNLS method used here is slow, so there are long delays
between clicks and interface updates.
Args:
shot: [int] shot number
fl_sav: [scipy.io.idl.readsav object] containing field line images
smoothing_param: [float] least-squares smoothing parameter
"""
time = acquire.gpi_series(shot, 'phantom2', 'time')
frames = acquire.video(shot, 'phantom2', sub=20, sobel=False)
frame_index = 0
fl_images = fl_sav.fl_image
fl_r = fl_sav.fieldline_r
fl_z = fl_sav.fieldline_z
rlcfs, zlcfs = acquire.lcfs_rz(shot)
efit_times, flux, flux_extent = acquire.time_flux_extent(shot)
efit_t_index = process.find_nearest(efit_times, time[0], ordered=True)
machine_x, machine_y = acquire.machine_cross_section()
inversion_func = scipy.optimize.nnls
geomatrix = np.transpose(np.array([fl.flatten() for fl in fl_images]))
geomatrix_smooth = np.concatenate((geomatrix,
smoothing_param*np.identity(len(fl_images))))
target = frames[0]
target_smooth = np.concatenate((target.flatten(), np.zeros(len(fl_images))))
inv = inversion_func(geomatrix_smooth, target_smooth)
r_space = np.linspace(min(fl_r), max(fl_r), 100)
z_space = np.linspace(min(fl_z), max(fl_z), 100)
r_grid, z_grid = np.meshgrid(r_space, z_space)
emissivity_grid = matplotlib.mlab.griddata(fl_r, fl_z, inv[0], r_grid,
z_grid, interp='linear')
reconstructed = geomatrix.dot(inv[0])
reconstructed = reconstructed.reshape((64,64))
target = target.reshape((64,64))
fig, ax = plt.subplots()
plt.subplot(221)
plt.title('Divertor camera view')
plasma_image = plt.imshow(target, cmap=plt.cm.gist_heat, origin='bottom')
plt.axis('off')
plt.colorbar()
plt.subplot(222)
plt.title('Reconstruction')
reconstruction_image = plt.imshow(reconstructed, cmap=plt.cm.gist_heat, origin='bottom')
plt.axis('off')
plt.colorbar()
plt.subplot(223)
plt.title('Reconstruction minus original')
error_image = plt.imshow(target - reconstructed, cmap=plt.cm.gist_heat, origin='bottom')
plt.axis('off')
plt.colorbar()
plt.subplot(224)
plt.title('Toroidal cross section')
emissivity_image = plt.pcolormesh(r_grid, z_grid, emissivity_grid)
colorbar = plt.colorbar()
colorbar.set_label('Relative emissivity')
plt.axis('equal')
plt.plot(machine_x, machine_y, color='gray')
l, = plt.plot(rlcfs[efit_t_index], zlcfs[efit_t_index], color='fuchsia')
plt.xlim([.49, .62])
plt.ylim([-.50, -.33])
plt.xlabel('R (m)')
plt.ylabel('Z (m)')
plt.contour(flux[efit_t_index], 100, extent=flux_extent)
phantom_slide_area = plt.axes([0.20, 0.02, 0.60, 0.03])
phantom_slider = Slider(phantom_slide_area, 'Camera frame', 0, len(frames)-1,
valinit=0)
phantom_slider.valfmt = '%d'
forward_button_area = plt.axes([0.95, 0.06, 0.04, 0.04])
forward_button = Button(forward_button_area, '>')
back_button_area = plt.axes([0.95, 0.01, 0.04, 0.04])
back_button = Button(back_button_area, '<')
def update_data(val):
global frame_index, emissivity_grid, reconstructed
frame_index = int(val)
target_smooth = np.concatenate((frames[frame_index].flatten(), np.zeros(len(fl_images))))
inv = inversion_func(geomatrix_smooth, target_smooth)
# inv = inversion_func(geomatrix, target)
reconstructed = geomatrix.dot(inv[0]).reshape((64,64))
emissivity_grid = matplotlib.mlab.griddata(fl_r, fl_z, inv[0], r_grid,
z_grid, interp='linear')
plasma_image.set_array(frames[frame_index])
reconstruction_image.set_array(reconstructed)
emissivity_image.set_array(emissivity_grid[:-1, :-1].ravel())
error_image.set_array(frames[frame_index]-reconstructed)
error_image.autoscale()
plasma_image.autoscale()
reconstruction_image.autoscale()
emissivity_image.autoscale()
efit_t_index = process.find_nearest(efit_times, time[frame_index], ordered=True)
l.set_xdata(rlcfs[efit_t_index])
l.set_ydata(zlcfs[efit_t_index])
fig.canvas.draw_idle()
def forward(event):
global frame_index
frame_index += 1
update_data(frame_index)
def backward(event):
global frame_index
frame_index -= 1
update_data(frame_index)
phantom_slider.on_changed(update_data)
forward_button.on_clicked(forward)
back_button.on_clicked(backward)
plt.tight_layout(rect=(0, .1, 1, .9))
plt.show()
fontsize=10)
#initiate speed meter
ax4 = plt.subplot(gs[5:9, 2])
smeter, = plt.bar(0, fuel, facecolor='#54dd5b', edgecolor='black')
plt.axis([0, fmeter.get_width() / 3, 0, yspeed])
ax4.set_xticks([0.5 * fmeter.get_width()])
ax4.set_xticklabels(['Speed'], color='white')
ax4.set_ylim([0, 15])
ax4.set_yticks([0])
ax4.set_yticklabels(["%2.1f m/s" % yspeed], color='white', fontsize=10)
ax4.yaxis.tick_right()
axThrust = plt.axes([0.18, 0.025, 0.65, 0.04])
thrust = Slider(axThrust, 'Thrust', 0.0, maxThrust, color='#8602e5')
thrust.valfmt = '%.f N'
ctypes.windll.user32.MessageBoxW(0, openMessage, 'Welcome!', 1)
fig.canvas.mpl_connect('key_press_event', press)
timeStamps = np.array([0])
heightStamps = np.array([height])
t = 0
del_t = 0
while height > 0:
if len(plt.get_fignums()) == 0:
sys.exit("Program closed")