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view.py
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view.py
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import sys
import os
import matplotlib.pyplot as plt
from matplotlib import animation
from matplotlib.widgets import Slider, Button, RadioButtons
import matplotlib.gridspec as gridspec
import numpy as np
import eqtools
import acquire
import process
def animate_phantom(shot, camera='phantom2', sub=20, blur=0, sobel=False,
interval=10, skip=1):
"""
Animate frames while displaying last closed flux surface and relevant 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
sobel: whether to apply a Sobel filter
interval: time between frames in ms
skip: number of frames to skip, 1 actually results in no frames skipped
"""
# Get time and frames
time = acquire.gpi_series(shot, camera, 'time')
frames = acquire.video(shot, camera, sub=sub, blur=blur, sobel=sobel)
# Phantom frame plot
gs = gridspec.GridSpec(3, 1, height_ratios=[3, 1, 1])
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.25)
plt.subplot(gs[0])
im = plt.imshow(frames[0], origin='lower', cmap=plt.cm.gist_heat)
plt.axis('off')
# H_alpha timeseries plot
time_ha2, ha2 = acquire.time_ha2(shot)
plt.subplot(gs[1]).locator_params(axis='y', nbins=4)
plt.plot(time_ha2, ha2)
plt.title('H-alpha')
vl1 = plt.axvline(time[0], color='r')
plt.xlim([time[0], time[-1]])
plt.ylim([0, np.max([s for i, s in enumerate(ha2)
if time[0] < time_ha2[i] < time[-1]])])
# Line average density plot
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]])
def init():
im.set_data(frames[0])
vl1.set_xdata(time[0])
vl2.set_xdata(time[0])
return [im, vl1, vl2]
def animate(i):
i *= skip
im.set_array(frames[i])
vl1.set_xdata(time[i])
vl2.set_xdata(time[i])
return [im, vl1, vl2]
dim = frames.shape
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=dim[0], interval=interval, blit=True)
plt.tight_layout(pad=1)
plt.show()
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()
def animate_frames(frames, cmap=plt.cm.gist_heat, disp=True, save=False, interval=5):
"""
Animate given frames.
Parameters
frames: NumPy array of frames with dimension (frame count, x pixels,
y pixels)
cmap: colormap e.g. plt.cm.gist_heat, plt.cm.gray
disp: whether to display animated figure
save: whether to save animated figure to 'out.gif'
interval: time interval between frames in ms
"""
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0)
im = plt.imshow(frames[0], origin='lower', cmap=cmap)
def init():
im.set_data(frames[0])
return [im]
def animate(i):
im.set_array(frames[i])
return [im]
plt.axis('off')
plt.tight_layout(pad=0, rect=(0, 0, 1, 1))
pos = ax.get_position(); pos.y0 = 0.; ax.set_position(pos)
anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=frames.shape[0], interval=interval,
blit=False)
if disp: plt.show()
if save: anim.save('out.mp4', writer='ffmpeg', fps=20)
def animate_shot(shot, camera='phantom2', cmap=plt.cm.gist_heat, disp=True,
save=False, interval=50):
"""
Animate frames for the given shot and camera.
Parameters
shot: int, shot number
camera: string e.g. 'phantom2', 'raspi2'
cmap: colormap e.g. plt.cm.gist_heat, plt.cm.gray
disp: whether to display animated figure
save: whether to save animated figure to 'out.gif'
interval: time interval between frames in ms
"""
frames = acquire.video(shot, camera)
animate_frames(frames, cmap=cmap, disp=disp, save=save, interval=interval)
def output_gif(frames):
"""
Save a set of frames as out.gif by producing temporary images and putting
them together using ImageMagick.
Parameters
frames: NumPy array with dimension (frame count, x pixels, y pixels)
"""
frames = frames[:, ::-1, :]
for i, frame in enumerate(frames):
plt.imsave('outframe_%06d.png' % i, frame, vmin=frame.min(),
vmax=frame.max(), cmap=plt.cm.gist_heat)
os.system('convert outframe_*.png -layers optimize -delay 2/100 out.gif && rm outframe_*.png')
def output_vid_frames(frames):
frames = frames[:, ::-1, :]
fig = plt.imshow(frames[0])
fig.set_cmap('hot')
plt.axis('off')
for i, frame in enumerate(frames):
fig.set_data(frame, vmin=frame.min(), vmax=frame.max())
plt.savefig('out/image%05d.png' % i, bbox_inches='tight')
#plt.imsave('out/image%05d.png' % i, frame, vmin=frame.min(), vmax=frame.max(), cmap=plt.cm.gist_heat)
def output_frames(shot, start, end, traces=True):
camera = 'phantom2'
time = acquire.gpi_series(shot, camera, 'time')
if type(start) is int:
start_frame = start
end_frame = end
else:
start_frame = process.find_nearest(time, start, ordered=True)
end_frame = process.find_nearest(time, end, ordered=True)
frames = acquire.video(shot, camera, sub=20)
folder_name = '%s-%d-%d' % (shot, start_frame, end_frame)
os.system('mkdir %s' % folder_name)
if not traces:
plt.figure()
im = plt.imshow(frames[start_frame], origin='lower', cmap=plt.cm.gist_heat,
vmin=0, vmax=130)
#plt.axis('off')
plt.colorbar()
for i in xrange(start_frame, end_frame):
im.set_data(frames[i])
#im.autoscale()
plt.savefig(folder_name + '/frame%05d.png' % i, bbox_inches='tight')
return
# LCFS data gathering
efit_tree = eqtools.CModEFIT.CModEFITTree(shot)
efit_times = efit_tree.getTimeBase()
rlcfs = efit_tree.getRLCFS()
zlcfs = efit_tree.getZLCFS()
efit_t_index = process.find_nearest(efit_times, time[0], ordered=True)
phantom_extent = acquire.extent(shot, camera)
# GPI, LCFS initial plotting
gs = gridspec.GridSpec(3, 1, height_ratios=[3, 1, 1])
#fig, ax = plt.subplots()
plt.figure()
plt.subplots_adjust(hspace=.5)
#plt.subplots_adjust(bottom=0.25)
plt.subplot(gs[0])
im = plt.imshow(frames[0], origin='lower', extent=phantom_extent,
cmap=plt.cm.gist_heat)
im.get_axes().locator_params(nbins=6)
l, = plt.plot(rlcfs[efit_t_index], zlcfs[efit_t_index], color='c')
plt.xlim(phantom_extent[0:2])
plt.ylim(phantom_extent[2:4])
time_ha2, ha2 = acquire.time_ha2(shot)
ha_max = np.max([s for i, s in enumerate(ha2)
if time[start_frame] < time_ha2[i] < time[end_frame]])
ha_min = np.min([s for i, s in enumerate(ha2)
if time[start_frame] < time_ha2[i] < time[end_frame]])
time_dens, dens = acquire.time_dens(shot)
dens_max = np.max(dens)
dens_min = np.min(dens)
for i in xrange(start_frame, end_frame):
# H-alpha
plt.subplot(gs[1]).locator_params(axis='y', nbins=4)
plt.title('H-alpha')
plt.plot(time_ha2, ha2)
plt.xlim([time[start_frame], time[end_frame]])
plt.ylim([ha_min, ha_max])
v1 = plt.axvline(time[i], color='r')
#plt.ylabel('[Units]')
# Line average density
plt.subplot(gs[2]).locator_params(axis='y', nbins=4)
plt.title('Line Average Density')
plt.plot(time_dens, dens)
plt.xlim([.5, 1.5])
plt.ylim([dens_min, dens_max])
plt.xlabel('Time (s)')
#plt.ylabel('[Units]')
v2 = plt.axvline(time[i], color='r')
im.set_data(frames[i])
im.autoscale()
plt.savefig(folder_name + '/frame%05d' % i)
v1.remove()
v2.remove()
def plot_field_lines(shot, fl_r, fl_z):
"""
Plot given field lines along with LCFS and psi_cont surfaces for given shot.
Parameters
shot: int, shot number
fl_r: array, field line r values
fl_z: array, field line z values
"""
efit_tree = eqtools.CModEFIT.CModEFITTree(shot)
efit_times = efit_tree.getTimeBase()
time = acquire.gpi_series(shot, 'phantom2', 'time')
efit_t_index = process.find_nearest(efit_times, time[0], ordered=True)
rlcfs, zlcfs = acquire.lcfs_rz(shot)
machine_x, machine_y = acquire.machine_cross_section()
# corners = acquire.frame_corners(shot, 'phantom2')
# corners_r, corners_z = [c[0] for c in corners], [c[1] for c in corners]
time, psirz, psiext = acquire.time_flux_extent(shot)
plt.figure()
plt.plot(fl_r, fl_z, 'b.')
plt.plot(plt.contour(psirz[efit_t_index], levels=np.arange(np.min(psirz),
np.max(psirz), .007), extent=psiext))
plt.plot(rlcfs[efit_t_index], zlcfs[efit_t_index], 'r-')
plt.plot(machine_x, machine_y, color='gray')
#plt.plot(1.020, -.265, 'go')
#plt.plot(corners_r, corners_z, 'go')
#plt.annotate('Aperture', (1.020, -.265))
#plt.annotate('Field lines', (fl_r[-1], fl_z[-1]))
#plt.annotate('View corners', corners[2])
plt.xlabel('R (m)')
plt.ylabel('Z (m)')
plt.axis('equal')
plt.show()
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()
if __name__ == '__main__':
try:
shot = int(sys.argv[1])
except:
shot = None
if shot:
slide_phantom(shot)
else:
print 'Try help(slide_phantom) and help(animate_phantom) for inspiration.'