def pred_animation(y_test, y_pred, is_save=False):
fig = plt.figure(figsize=(15, 10))
ims = []
plt.imshow(room_image, extent=(0, 7.8, 0, 8.8))
# Plot antenna position
plt.scatter(7.25, 1.5, color='green', marker=7, s=500)
plt.scatter(7.2, 7.1, color='green', marker=7, s=500)
for m in range(3):
plt.scatter(0.6, 2.763 - m * 0.063, color='cyan', marker=7, s=500)
# Plot true position and predicted position (Animation)
for i in range(len(y_test)):
im_test = [
plt.scatter(y_test[i, 0],
y_test[i, 1],
color='black',
marker='X',
s=1200)
]
im_pred = [
plt.scatter(y_pred[i, 0],
y_pred[i, 1],
color='blue',
s=1200,
alpha=0.7)
]
ims.append(im_test + im_pred)
plt.xlim(0, 7.8)
plt.ylim(0, 8.8)
ani = ArtistAnimation(fig, ims, interval=500)
if is_save:
ani.save(os.path.join(FIGURE_SAVE_PATH, 'pred_anim.mp4'),
writer="ffmpeg")
plt.show()
def animate(theta1s, AB, AD, BC, CD, variation=0):
rA, rD = 0, AD
fig, ax = plt.subplots()
container = []
for theta1 in theta1s:
rB, rC = pos(theta1, AB, AD, BC, CD)[variation]
link1, = ax.plot(np.real([rA, rB]), np.imag([rA, rB]), 'ro-')
link2, = ax.plot(np.real([rB, rC]), np.imag([rB, rC]), 'go-')
link3, = ax.plot(np.real([rD, rC]), np.imag([rD, rC]), 'bo-')
A = ax.text(np.real(rA), np.imag(rA) + .01, r'$A$')
B = ax.text(np.real(rB), np.imag(rB) + .01, r'$B$')
C = ax.text(np.real(rC), np.imag(rC) + .01, r'$C$')
D = ax.text(np.real(rD), np.imag(rD) + .01, r'$D$')
container.append([link1, link2, link3, A, B, C, D])
from matplotlib.animation import ArtistAnimation
ani = ArtistAnimation(fig, container, interval=20, blit=True)
ax.set_ylim(ymax=CD + .05)
ax.grid()
ax.set_aspect('equal')
ani.save('mechanism_1_{0:d}.mp4'.format(variation),
fps=60,
bitrate=5000,
dpi=400)
def plot_paths(layout, paths):
images = []
cmap = colors.ListedColormap(['white', 'black', 'red', 'green', 'blue'])
lengths = [len(path) for path in paths]
lengths.sort()
longest_path = lengths[-1]
fig = plt.figure()
for i in range(longest_path):
steps = []
for path in paths:
if path.get(i) != None:
steps.append(path.get(i))
# print(steps)
layout_arr = transform_array_to_int(layout, steps)
img = plt.pcolor(layout_arr[::-1],
cmap=cmap,
edgecolors='k',
linewidths=1)
images.append([img])
if i == 0:
plt.savefig('pics/start.png')
images.insert(0, images[1])
images.append(images[-1])
animation = ArtistAnimation(fig, images, interval=250)
print('Animation steps:', len(images))
animation.save('video/anim.mp4', dpi=800)
def animate(case):
from matplotlib.animation import ArtistAnimation
phi1s = np.linspace(0, 2 * np.pi, 300)
fig, ax = plt.subplots()
rA, container = 0, []
for phi in phi1s:
rB, rC, rD, rE, rF = pos(phi, case)
link1, = ax.plot(np.real([rA, rB]), np.imag([rA, rB]), 'ro-')
link2, = ax.plot(np.real([rB, rC]), np.imag([rB, rC]), 'go-')
link3, = ax.plot(np.real([rC, rE]), np.imag([rC, rE]), 'bo-')
link4, = ax.plot(np.real([rE, rF]), np.imag([rE, rF]), 'ko-')
ax.plot(np.real(rD), np.imag(rD), 'bo-')
ax.plot(np.real(rF), np.imag(rF), 'b,-')
A = ax.text(np.real(rA), np.imag(rA) + .002, r'$A$')
B = ax.text(np.real(rB), np.imag(rB) + .002, r'$B$')
C = ax.text(np.real(rC), np.imag(rC) + .002, r'$C$')
D = ax.text(np.real(rD), np.imag(rD) + .002, r'$D$')
E = ax.text(np.real(rE), np.imag(rE) + .002, r'$E$')
F = ax.text(np.real(rF), np.imag(rF) + .002, r'$F$')
container.append([link1, link2, link3, link4, A, B, C, D, E, F])
ani = ArtistAnimation(fig, container, interval=20, blit=True)
ax.set_aspect('equal')
ax.set_ylim(ymax=case[1] + .02)
ax.grid()
plt.show()
ani.save('mechanism_3.mp4', fps=60, bitrate=5000, dpi=400)
def animate(phi1s, variation=0, fps=60, AB=1, BC=1):
"""
Animate movement of the mechanism
"""
rA = 0
fig, ax = plt.subplots()
container = []
for phi in phi1s:
rB, rC, rBC = pos(phi, AB, BC)[variation]
link1, = ax.plot(np.real([rA, rB]), np.imag([rA, rB]), 'ro-')
link2, = ax.plot(np.real([rB, rC]), np.imag([rB, rC]), 'go-')
A = ax.text(np.real(rA), np.imag(rA) + .07, 'A')
B = ax.text(np.real(rB), np.imag(rB) + .07, 'B')
C = ax.text(np.real(rC), np.imag(rC) + .07, 'C')
container.append([link1, link2, A, B, C])
ax.plot(np.real(rC), np.imag(rC), 'b,-')
from matplotlib.animation import ArtistAnimation
ani = ArtistAnimation(fig, container, interval=fps, blit=True)
ax.grid()
ax.set_aspect('equal')
ani.save('R_RRT.mp4', fps=60, bitrate=5000, dpi=400)
def animate(case):
from matplotlib.animation import ArtistAnimation
theta1s = np.linspace(0, 2*np.pi, 300)
fig, ax = plt.subplots()
rA, container = 0, []
for theta1 in theta1s:
rB, rC, rD, rE, rD2 = pos(theta1, case)
link1, = ax.plot(np.real([rA, rB]), np.imag([rA, rB]), 'ro-')
link31, = ax.plot(np.real([rC, rD]), np.imag([rC, rD]), 'go-')
link32, = ax.plot(np.real([rC, rD2]), np.imag([rC, rD2]), 'g-')
link4, = ax.plot(np.real([rD, rE]), np.imag([rD, rE]), 'bo-')
ax.plot(np.real(rE), np.imag(rE), 'b,-')
A = ax.text(np.real(rA), np.imag(rA)+.02, r'$A$')
B = ax.text(np.real(rB), np.imag(rB)+.02, r'$B$')
C = ax.text(np.real(rC), np.imag(rC)+.02, r'$C$')
D1 = ax.text(np.real(rD), np.imag(rD)+.02, r'$D_1$')
D2 = ax.text(np.real(rD2), np.imag(rD2)+.02, r'$D_2$')
E = ax.text(np.real(rE), np.imag(rE)+.02, r'$E$')
container.append([link1, link31, link32, link4, A, B, D2, C, D1, E])
link3, = ax.plot(np.real(rC), np.imag(rC), 'ro')
ani = ArtistAnimation(fig, container, interval=20, blit=True)
ax.set_aspect('equal')
ax.grid()
plt.show()
ani.save('mechanism_9.mp4', fps=60, bitrate=5000, dpi=400)
def animate_vibration_mode_stress(self, k, alpha=1, l=1, show=None, savename=None,
playtime=5, fps=60, repeat_delay=0, title=None):
if k > self.num_eigenpairs - 1:
raise ValueError(f"Too high an eigen-number. Have only solved for {self.num_eigenpairs} eigenpairs.")
displacement_vec = self.retrieve_vibration_eigenvector(k)
N_frames = int(playtime * fps)
ts = np.linspace(0, 2*np.pi, N_frames)
max_stretch = self.find_max_stress(displacement=displacement_vec*alpha)
# Set a bigger font size for text:
plt.rcParams.update({'font.size': 20})
norm = mpl.colors.Normalize(vmin=-max_stretch, vmax=max_stretch)
fig, ax = plt.subplots(figsize=(1.5*16,1.5*9))
plt.subplots_adjust(left=0.05, bottom=0.05, right=0.99, top=0.94)
ax.set_xlabel("x [m]")
ax.set_ylabel("y [m]")
if title is None:
fig.suptitle(f"Vibration mode {k} for {self.area} with {len(self.triang)} Elements")
elif type(title) is str:
fig.suptitle(title)
else:
pass
artists = [self.display_mesh_stress(displacement=alpha*np.sin(l*t)*displacement_vec,
norm=norm, show=False, ax=ax).findobj() for t in ts]
ani = ArtistAnimation(fig, artists, interval=1000//fps, repeat_delay=repeat_delay, repeat=True, blit=True)
cbar = fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=None), ax=ax)
cbar.ax.get_yaxis().labelpad = 50
cbar.set_label(r"Mean Total Stress $\sigma$ [Pa]", rotation=270, fontsize=24)
if savename is not None:
ani.save(f"root/project_2/animations/{savename}.mp4")
if show is None:
if savename is None:
show = True
else:
show = False
if show:
plt.show()
return
# Clean up memory
fig.clear()
plt.close(fig)
del fig, ax, artists, ani, norm, cbar
import gc
gc.collect()
return
def pred_evolution_gif(fig,
frames_list,
interval=300,
save_dir='',
save=True,
no_margins=True,
show=False):
print("\n\n\n\n=================")
print("checking for ffmpeg...")
if not os.path.isfile('./../../../opt/conda/bin/ffmpeg'):
print("please 'pip install ffmpeg' to create gif")
print("gif not created")
else:
print("ffmpeg found")
print("creating the gif ...\n")
gif = ArtistAnimation(fig, frames_list, interval,
repeat=True) # create gif
if save:
if no_margins:
plt.tight_layout()
plt.gca().set_axis_off()
plt.subplots_adjust(top=1,
bottom=0,
right=1,
left=0,
hspace=0,
wspace=0)
plt.margins(0, 0)
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
if save_dir == '':
time = datetime.now().strftime("%Y%m%d-%H%M%S")
save_dir = 'results/gif' + time + '.gif'
plt.rcParams[
'animation.ffmpeg_path'] = r'//opt//conda//bin//ffmpeg' # set directory of ffmpeg binary file
Writer = animation.writers['ffmpeg']
ffmwriter = Writer(fps=1000 // interval,
metadata=dict(artist='Me'),
bitrate=1800) #set the save writer
gif.save('results/temp_video.mp4', writer=ffmwriter)
codeBASH = f"ffmpeg -i 'results/temp_video.mp4' -loop 0 {save_dir}" #convert mp4 to gif
os.system(codeBASH)
os.remove("results/temp_video.mp4")
plt.close('all')
if show:
plt.show()
plt.close('all')
print("\n\n=================")
print('done\n\n')
def save(self, filename, fps: float = 10):
"""
Save Gif.
Args:
filename: Filename of the Gif
fps: Frames per second
"""
animation = ArtistAnimation(self.fig, self.images)
animation.save(filename, writer="imagemagick", fps=fps)
def gif_from_path(
path=None,
figsize=(10, 10),
fps=0.5,
interval=500,
repeat_delay=1000,
filename=None,
dpi=400,
):
"""
Create an animated gif from a director of image files.
Parameters
----------
path :str, required
path to directory of images
figsize : tuple, optional
output figure size passed to matplotlib.pyplot
fps : float, optional
frames per second
interval : int, optional
interval between frames in miliseconds, default 500
repeat_delay : int, optional
time before animation repeats in miliseconds, default 1000
filename : str, required
output file name
dpi : int, optional
image dpi passed to matplotlib writer
"""
assert filename, "You must provide an output filename ending in .gif"
imgs = os.listdir(path)
imgs.sort(key=lambda var: [
int(x) if x.isdigit() else x
for x in re.findall(r"[^0-9]|[0-9]+", var)
])
fig, ax = plt.subplots(figsize=figsize)
ax.axis("off")
ims = []
for i in imgs:
c = plt.imread(str(PurePath(path, i)))
im = plt.imshow(c, animated=True)
ims.append([im])
writer = PillowWriter(fps=fps)
ani = ArtistAnimation(fig,
ims,
interval=interval,
blit=True,
repeat_delay=repeat_delay)
plt.tight_layout()
ani.save(filename, writer=writer, dpi=dpi)
def save_gif(images, n=0, fname=None):
"""saves a series of images as a gif"""
fig = plt.figure()
plt.axis('off')
ims = []
for img in images:
im = plt.imshow(img, animated=True, vmin=0., vmax=1.)
ims.append([im])
anim = ArtistAnimation(fig, ims, interval=100, repeat=False)
fname = fname if fname is not None else 'figs/cav_transition/test{}.gif'.format(n)
anim.save(fname)
def __init__(self, plots, interval=200, repeat_delay=None,
repeat=True, blit=False):
figure, artists = self._draw_plots(plots)
ArtistAnimation.__init__(
self,
figure,
artists,
interval=interval,
repeat_delay=repeat_delay,
repeat=repeat,
blit=blit
)
def show_animation(self) -> None:
"""Displays the animation in a pop-up. Optionally saves the animation."""
use("TkAgg")
fig = plt.figure()
ims = self._generate_images()
ani = ArtistAnimation(fig, ims, interval=50, blit=True)
should_save = input('Do you want to save the animation? [Y/N]\n')
if should_save == 'y' or should_save == 'Y':
ani.save('julia_sets.mp4', writer="ffmpeg")
plt.title(f'Animated Julia sets with c = {self.anim_constant}*e^(i*a)')
plt.show()
def gen_visualisation():
"""
Generates an .mp4 movies representing the restructuring
of the initial graph based on output files from spring.cpp
"""
fig = plt.figure()
try:
center = sys.argv[1] # ensure test_file specified
except IndexError:
print('Usage: genvis.py test_file')
sys.exit(1)
nodes = 0
frames = 0
data = []
images = []
# load the initial graph as the first frame
with open(center, 'r') as f:
for i, l in enumerate(f):
if i == 0:
frames = int(l.strip('\n'))
elif i == 1:
nodes = int(l.strip('\n'))
elif 1 < i <= nodes + 1:
x, y = map(float, l.strip('\n').split(' '))
data.append([x, y])
elif i > nodes + 1:
break
scat = plt.scatter(*zip(*data), s=10)
# add the frame to list
images.append([scat])
# loop over all other output files and generate frames
for i in range(frames):
data = []
with open(str(i) + '.out', 'r') as f:
for l in f:
x, y = map(float, l.strip('\n').split(' '))
data.append([x, y])
scat = plt.scatter(*zip(*data), s=10)
images.append([scat])
# create animation
line_anim = ArtistAnimation(fig, images, interval=50, blit=True)
line_anim.save('graph_layout.mp4')
# plt.show()
# cleanup output files
files = [f for f in os.listdir(".") if f.endswith(".out")]
for f in files:
os.remove(f)
def demo(self, performance, limit, defaultPause=0.1, interval=0.1):
import matplotlib.pyplot as plt
from matplotlib.animation import ArtistAnimation
figure = plt.figure()
artists = []
timer = 0.0
self.leds.clear()
action = performance(self.leds)
while timer < limit:
try:
pause = next(action)
except StopIteration:
break
if pause is None:
pause = defaultPause
art = self.leds.plot(show=False)
num = int((pause) / interval)
if num == 0:
continue
artists += [[art]] * num
timer += pause
ani = ArtistAnimation(figure,
artists,
interval=interval * 1000,
repeat=False,
blit=True) # noqa
plt.show()
def animate(phi1s):
from matplotlib.animation import ArtistAnimation
fig, ax = plt.subplots()
container = []
for phi in phi1s:
rB, rD, rF, rG = pos(phi)
pinA, = ax.plot(rA[0], rA[1], 'bo')
pinC, = ax.plot(rC[0], rC[1], 'bo')
link1, = ax.plot([rA[0], rB[0]], [rA[1], rB[1]], 'ro-')
link3, = ax.plot([rF[0], rD[0]], [rF[1], rD[1]], 'go-')
link5, = ax.plot([rA[0], rG[0]], [rA[1], rG[1]], 'bo-')
A = ax.text(rA[0]+.05, rA[1]+.05, 'A', fontsize=12)
B = ax.text(rB[0]+.05, rB[1]+.05, 'B', fontsize=12)
C = ax.text(rC[0]+.05, rC[1]+.05, 'C', fontsize=12)
D = ax.text(rD[0]+.05, rD[1]+.05, 'D', fontsize=12)
F = ax.text(rF[0]+.05, rF[1]+.05, 'F', fontsize=12)
G = ax.text(rG[0]+.05, rG[1]+.05, 'G', fontsize=12)
container.append([pinA, pinC, link1, link3, link5, A, B, C, D, F, G])
ax.grid()
ax.set_aspect('equal')
ani = ArtistAnimation(fig, container, interval=20, blit=True)
def show_anim(set_num, anim_num):
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import ArtistAnimation
anims = _read_hdr()
s = anims.sets[set_num]
anim = s.animations[anim_num]
images = [anims.render_pixelmap(frame) for frame in anim.frames]
fps = anim.fps
borders = np.array([[x, x + w, -y - h, -y] for frame in anim.frames
for x, y, w, h in [frame.origin + frame.shape]])
extremes = ((borders[:, 0].min(), borders[:, 1].max()),
(borders[:, 2].min(), borders[:, 3].max()))
fig, ax = plt.subplots()
artists = [[plt.imshow(image, animated=True, extent=borders[i])]
for i, image in enumerate(images)]
ani = ArtistAnimation(fig, artists, interval=1000.0 / fps, blit=True)
ax.axis("off")
ax.set_xlim(extremes[0])
ax.set_ylim(extremes[1])
plt.show()
def animation(*args, **kwargs):
"""
Artist animation wrapper to avoid another import
"""
return ArtistAnimation(*args, **kwargs)
def animate(self, *args, **kwargs) -> ArtistAnimation:
"""Animate the snapshots taken.
Uses matplotlib.animation.ArtistAnimation
Returns
-------
ArtistAnimation
"""
return ArtistAnimation(self._figure, self._photos, *args, **kwargs)
def run_oned_linear_convection(method, nx, nt, x_min, x_max, dx, dt, c):
# Generate figure for animation
fig = pyplot.figure()
ax = fig.add_subplot()
frames = []
# Generate initial data
u = get_oned_data(InitialDataType.SQUARE, nx=nx, dx=dx)
# Apply `method` for `nt` times
# and save each frame to create an animation
for i in range(nt):
oned_linear_convection(u=u, nx=nx, dx=dx, dt=dt, c=c)
frames.append(ax.plot(np.linspace(x_min, x_max, nx), u, "r"))
# Now create the animation at 1 frame every 25 milliseconds
animation = ArtistAnimation(fig, frames, repeat=False, interval=25)
# And render to an mp4
animation.save(f"{method.__name__}.mp4")
def create_animation(df, img, include_backwards=True, fps=24, n_seconds=2, figsize=(8, 8), repeat=True):
"""Create animation from a displacement field.
Parameters
----------
df : DisplacementField
Instance of the ``DisplacementField`` representing the coordinate transformation.
img : np.ndarray
Image.
include_backwards : bool
If True, then animation also played backwards after its played forwards.
fps : int
Frames per second.
n_seconds : int
Number of seconds to play the animation forwards.
figsize : tuple
Size of the figure.
repeat : bool
If True, then animation always replayed at the end.
Returns
-------
ani : matplotlib.animation.ArtistAnimation
Animation showing the transformation.
Notes
-----
To enable animation viewing in a jupyter notebook write:
```
from matplotlib import rc
rc('animation', html='html5')
```
"""
n_frames = int(n_seconds * fps)
interval = (1 / fps) * 1000
frames = []
fig = plt.figure(figsize=figsize)
plt.axis('off')
for i in range(n_frames + 1):
df_new = df * (i / n_frames)
warped_img = df_new.warp(img)
frames.append([plt.imshow(warped_img, cmap='gray')])
if include_backwards:
frames += frames[::-1]
ani = ArtistAnimation(fig, frames, interval=interval, repeat=repeat)
return ani
def sim_show(self):
fig = plt.figure()
artists = []
for config in self.config_history:
plot = plt.imshow(config)
artists.append([plot])
anima = ArtistAnimation(fig, artists, interval=200, repeat=False)
plt.show(anima)
def create_animation(self):
"""
Upper function. Calls other functions and creates the animation object
:return: None
"""
self.init_subplots()
self.ln = []
self.ln.extend(self.draw_network())
self.ln.extend(self.plot_node_ids())
self.ln.extend(self.plot_delay())
self.artists = [self.ln]
self.artists.extend(self.create_artists())
self.animation = ArtistAnimation(self.fig,
self.artists,
interval=self.interval,
repeat=False)
def main():
fig = plt.figure()
plt.axis([0, SIZE, 0, SIZE]) #xmin xmax ymin ymax
plt.xticks([])
plt.yticks([])
x, y, z = random_direction(math.floor(SIZE / 2), math.floor(SIZE / 2),
random_step_size())
while z != 1:
x, y, z = random_direction(x, y, random_step_size())
animation = ArtistAnimation(fig, images, interval=100, repeat=0)
plt.show()
def animate_vibration_mode(self, k, alpha=1, l=1, show=None, savename=None, playtime=5, fps=60, repeat_delay=0, title=None):
if k > self.num_eigenpairs - 1:
raise ValueError(f"Too high an eigen-number. Have only solved for {self.num_eigenpairs} eigenpairs.")
displacement_vec = self.retrieve_vibration_eigenvector(k)
N_frames = playtime * fps
ts = np.linspace(0, 2*np.pi, N_frames)
fig, ax = plt.subplots(figsize=(16, 16))
if title is None:
fig.suptitle(f"Eigen vibration mode {k}", fontsize=18)
else:
fig.suptitle(title, fontsize=18)
artists = [self.display_mesh(displacement=alpha*np.sin(l*t)*displacement_vec,
show=False, ax=ax) for t in ts]
ani = ArtistAnimation(fig, artists, interval=1000//fps, repeat_delay=repeat_delay, repeat=True, blit=True)
if savename is not None:
ani.save(f"root/project_2/animations/{savename}.mp4")
if show is None:
if savename is None:
show = True
else:
show = False
if show:
plt.show()
return
# Clean up memory
fig.clear()
plt.close(fig)
del fig, ax, artists, ani
import gc
gc.collect()
return
def draw_solver_progress(progress_mats,
display_with_ipython=True,
img_filename=None,
imagemagick_path=None,
imagemagick_extra_args=None):
'''
Produce a GIF of solution progress using matplotlib, requires imagemagick.
Parameters
----------
progress_mats: list of 2D arrays, required
List of matrices showing progression of brute force solver. Produced by
solve_nonogram when return_iterations is set to True
display_with_ipython: bool, optional, default: True
Whether or not to display image using IPython.display.Image (for jupyter
notebook or console)
img_filename: str or None, optional, default=None
path to output GIF file. If not specified, a temporary file will be
created for display.
'''
fig, ax = plt.subplots(figsize=(10, 10))
ax.autoscale()
imgs = [draw_solution_so_far(mat, ax) for mat in progress_mats]
anim = ArtistAnimation(fig, imgs, interval=500, repeat_delay=1000)
plt.axis('square')
plt.axis('off')
if img_filename is None:
handle, img_filename = mkstemp(suffix='.gif')
os.close(handle)
if imagemagick_path:
plt.rcParams["animation.convert_path"] = imagemagick_path
anim.save(img_filename, writer='imagemagick', extra_args=imagemagick_extra_args)
plt.close()
if display_with_ipython:
from IPython.display import Image, display
shutil.copy2(img_filename, img_filename + '.png')
display(Image(filename=img_filename + '.png'))
def play_video(self, t_ini=0, t_final=np.inf, grayscale = True, save=False):
""" Play a video of radar images between t_ini and t_final
grayscale: if False automatic coloration of images is used
save: if True, save the video as a .mp4
"""
# Handling pause/resume event when clicking on the video
anim_running = True
def onClick(event):
nonlocal anim_running
if anim_running:
ani.event_source.stop()
anim_running = False
else:
ani.event_source.start()
anim_running = True
times = self.get_timestamps(t_ini, t_final)
images = []
fig = plt.figure(facecolor='black')
fig.set_figwidth(8)
fig.canvas.mpl_connect('button_press_event', onClick)
ax = plt.axes()
[ax.spines[spine].set_color('white') for spine in ax.spines]
ax.set_facecolor("black")
ax.tick_params(colors='black')
print("Creating video...")
for t in times:
if grayscale:
images.append([ax.imshow(Image.fromarray(self.heatmaps[t].img), cmap='gray', vmin=0, vmax=255), ax.text(0.6,0.8,str(round(t,2)), color='white')])
else:
images.append([ax.imshow(Image.fromarray(self.heatmaps[t].img)), ax.text(0.6,0.8,str(round(t,2)), color='white')])
ani = ArtistAnimation(fig, images, interval=100, blit=False, repeat_delay=1000)
if save:
print("Saving video: "+str(self.src) + '.mp4')
os.makedirs(os.path.dirname('Videos/' + str(self.src) + '.mp4'), exist_ok=True)
ani.save('Videos/' + str(self.src) + '.mp4', fps=10, dpi=200, savefig_kwargs={'facecolor': 'black'})
plt.show()
return ani
def main():
robotmap = robot_map(20, 20, 0.09, 0.9)
robotmap.generate_map()
row, col = robotmap.get_start_position()
target_row, target_col = robotmap.get_target_position()
ret, dfs_path, res_path = dijkstra_search(robotmap, row, col, target_row, target_col)
print('start position:', row, col)
print('target position:', target_row, target_col)
dfs_path = np.array(dfs_path)
res_path = np.array(res_path)
if ret == False:
print('can not find a path')
sys.exit(0)
else:
print('success to find a path')
fig = plt.figure()
ax = plt.axes()
ax.set_xlim([0, robotmap.shape[0]])
ax.set_xlabel('X')
ax.set_ylim([0, robotmap.shape[1]])
ax.set_ylabel('Y')
ax.set_title('maze')
images = []
image = robotmap.get_image()
# draw start and target
image[row, col] = 50
image[target_row, target_col] = 200
im = plt.imshow(image, cmap=plt.cm.get_cmap('jet'), animated=True, interpolation='nearest')
images.append([im])
# draw path
for i in range(1, dfs_path.shape[0] - 1):
image[dfs_path[i, 0], dfs_path[i, 1]] = 100
im = plt.imshow(image, cmap=plt.cm.get_cmap('jet'), animated=True, interpolation='nearest')
images.append([im])
image = robotmap.get_image()
image[row, col] = 50
image[target_row, target_col] = 200
for i in range(1, res_path.shape[0] - 1):
image[res_path[i, 0], res_path[i, 1]] = 150
for i in range(10):
im = plt.imshow(image, cmap=plt.cm.get_cmap('jet'), animated=True, interpolation='nearest')
images.append([im])
ani = ArtistAnimation(fig, images, interval=len(images), blit=True,
repeat_delay=100)
# ani.save('dijkstra_search.gif', dpi=80, writer='imagemagick')
plt.show()
def generate_gif(self, input_chord, output_file, framerate):
"""
Parameters
----------
input_chord: iterable
output_file: str
framerate: int
Returns
-------
"""
img_matrices = self.play_chords(input_chord)
fig = plt.figure(figsize=(20, 10))
artists = [[plt.imshow(i, animated=True, cmap='gray')]
for i in img_matrices]
ani = ArtistAnimation(fig, artists, interval=framerate, blit=True)
ani.save(output_file, writer='imagemagick')
def save_ds_video_path(date, cube_list, dated, timed, path):
#plt.ion()
f1 = plt.figure(figsize=(3, 3))
ax = f1.add_subplot(111)
struct = extract_cube_path(date, cube_list)
cube_ds = dark_subtract_cube(dated, timed, struct)
cube_ds = cube_ds[400:, :, :]
images = []
texts = [
"AO188 off, SCExAO off", "AO188 on, SCExAO off", "AO188 on, SCExAO on"
]
for i in xrange(cube_ds.shape[0]):
print i
imtemp = cube_ds[i, 68:188, 100:220]
imtemp = np.log10(imtemp)
imtemp = np.nan_to_num(imtemp)
im = ax.imshow(imtemp,
cmap=cmaps.inferno,
clim=(0.0, 4.0),
interpolation="bicubic")
if i < 759:
j = 0
elif (i > 758) and (i < 1600):
j = 1
else:
j = 2
ax.annotate(texts[j], xy=(10, 110), xytext=(10, 110), color='white')
images.append((im, ))
anim = ArtistAnimation(f1, images, interval=25, blit=False)
anim.save(path, dpi=150)
return None
params.append((0.16, 0.08, 0.060, 0.062)) # Coral
params.append((0.19, 0.05, 0.060, 0.062)) # Fingerprint
params.append((0.10, 0.10, 0.018, 0.050)) # Spirals
params.append((0.12, 0.08, 0.020, 0.050)) # Spirals Dense
params.append((0.10, 0.16, 0.020, 0.050)) # Spirals Fast
params.append((0.16, 0.08, 0.020, 0.055)) # Unstable
params.append((0.16, 0.08, 0.050, 0.065)) # Worms 1
params.append((0.16, 0.08, 0.054, 0.063)) # Worms 2
params.append((0.16, 0.08, 0.035, 0.060)) # Zebrafish
(du, dv, f, k) = params[2]
dt = 1.
ims = []
fig = figure()
axis('off')
N = 20000
for ind in range(N):
u, v = u + (du * laplace(u, mode='wrap') - u * v ** 2 + f * (1. - u)) * dt, v + (
dv * laplace(v, mode='wrap') + u * v ** 2 - (k + f) * v) * dt
if ind % 10 == 0:
print(str(ind) + "/" + str(N))
ims.append([imshow(v)])
ani = ArtistAnimation(fig, ims, interval=10, blit=True, repeat_delay=1000)
Writer = writers['ffmpeg']
writer = Writer(fps=100, metadata=dict(artist='Laurent Garcin'), bitrate=18000)
ani.save('morpho.mp4', writer=writer)
show()
fig = plt.figure(figsize=(5, 5))
for epoch in range(nb_epoch):
# shuffle X_train and calculate P
if epoch % shuffle_interval == 0:
X = X_train[np.random.permutation(n)[:m]]
P = calculate_P(X)
# train
loss = 0
for i in xrange(0, n, batch_size):
loss += model.train_on_batch(X[i:i+batch_size], P[i:i+batch_size])
print "Epoch: {}/{}, loss: {}".format(epoch+1, nb_epoch, loss / batch_num)
# visualize training process
pred = model.predict(X_test)
img = plt.scatter(pred[:, 0], pred[:, 1], c=y_test,
marker='o', s=3, edgecolor='')
images.append([img])
ani = ArtistAnimation(fig, images, interval=100, repeat_delay=2000)
ani.save("mlp_process.mp4")
plt.clf()
fig = plt.figure(figsize=(5, 5))
pred = model.predict(X_test)
plt.scatter(pred[:, 0], pred[:, 1], c=y_test, marker='o', s=4, edgecolor='')
fig.tight_layout()
plt.savefig("mlp_result.png")
def _step(self, *a,**k):
artists = self._framedata[0]
self.updateArtists(artists)
AA._step(self,*a,**k)
nb = 10
z = uniform(-3, 3, nb) + 1j * uniform(-3, 3, nb)
a = 2 + 2j
b = 3 - 2j
Z = a * (z + normal(0, .01) + 1j * normal(0, .01)) + b
z0 = mean(z)
Z0 = mean(Z)
zz = z - z0
ZZ = Z - Z0
aa = sum(zz.conjugate() * ZZ) / sum(zz * zz.conjugate())
bb = Z0 - aa * z0
center = bb / (1 - aa)
mod = absolute(aa)
arg = angle(aa)
fig = figure()
ims = []
for t in linspace(0, 1, 100):
m = (1 - t) + t * mod
a = t * arg
a_current = m * exp(1j * a)
current = center + a_current * (z - center)
# current = (1 - t) * z + t * (aa * z + bb)
ims.append((scatter(z.real, z.imag, color='blue'), scatter(Z.real, Z.imag, color='red'),
scatter(current.real, current.imag)))
im_ani = ArtistAnimation(fig, ims, interval=50, repeat_delay=3000, blit=True)
im_ani.save('test.mp4')
show()
# plot a frame of the graph
voltageLine, currentLine = pylab.plot(timeLine[:i+1], axon.Vm[:i+1], 'b-', timeLine[:i+1], curr[:i+1], 'g-')
if k == 1: # have only 1 legend
pylab.legend([voltageLine, currentLine], ["Response from cell", "Impulse current"])
pylab.title('Effective Current ' + "{:6.3f}".format(effectiveCurrent) + u' µA/cm$^2$')
pylab.ylabel('Membrane Potential (mV)')
pylab.xlabel('Time (ms)')
pylab.ylim([-20,120])
pylab.xlim([0,60])
lines += [voltageLine, currentLine]
if i % 25 == 0:
# don't wanna print a lot cuz it slows things down
print "Time: " + str(i*dt) + " ms"
images.append(tuple(lines))
pylab.tight_layout()
anim = ArtistAnimation(animationFigure, images, interval = 40, blit = True)
print "Saving animation..."
anim.save("currentComparisons.mp4", extra_args=['-vcodec', 'libx264'])
pylab.show()
# pylab.figure()
# pylab.plot(timeLine, Vm, timeLine, I)
# pylab.title('Hodgkin-Huxley Example')
# pylab.ylabel('Membrane Potential (mV)')
# pylab.xlabel('timeLine (msec)')
# pylab.savefig("model.jpg")
sodiumCurrent = sodiumConductance * (Vm[i-1] - sodiumPotential)
potassiumCurrent = potassiumConductance * (Vm[i-1] - potassiumPotential)
leakageCurrent = leakageConductance * (Vm[i-1] - leakagePotential)
Vm[i] = Vm[i-1] + (I[i-1] - sodiumCurrent - potassiumCurrent - leakageCurrent) * dt / Cm
# update status
if i % 25 == 0:
# update status every 25 frames (about a second of video).
# don't wanna print a lot cuz it slows things down
print "Time: " + str(i*dt) + ", Saving frame " + str(i)
# plot a frame of the graph
voltageLine, currentLine = pylab.plot(timeLine[:i+1], Vm[:i+1], 'b-', timeLine[:i+1], I[:i+1], 'g-')
pylab.legend([voltageLine, currentLine], ["Response from cell", "Impulse current"])
pylab.title('Hodgkin-Huxley Model of Action Potentials')
pylab.ylabel('Membrane Potential (mV)')
pylab.xlabel('Time (ms)')
images.append((voltageLine, currentLine))
anim = ArtistAnimation(animationFigure, images, interval = 50, blit = True)
print "Saving animation"
anim.save("model.mp4", dpi=200, extra_args=['-vcodec', 'libx264'])
pylab.figure()
pylab.plot(timeLine, Vm, timeLine, I)
pylab.title('Hodgkin-Huxley Example')
pylab.ylabel('Membrane Potential (mV)')
pylab.xlabel('timeLine (msec)')
pylab.savefig("model.jpg")
a, b, c = random(size), random(size), random(size)
alpha, beta, gamma = 1., 1., 1.
Da, Db, Dc = .1, .1, .1
ims = []
fig = figure(figsize=(4, 4))
axis('off')
kernel = [[1, 1, 1], [1, -8, 1], [1, 1, 1]]
def constrain(v):
v[v < 0.] = 0.
v[v > 1.] = 1.
return v
N = 500
for ind in range(N):
a, b, c = constrain(a + Da * convolve(a, kernel) + a * (alpha * b - gamma * c)), \
constrain(b + Db * convolve(b, kernel) + b * (beta * c - alpha * a)), \
constrain(c + Dc * convolve(c, kernel) + c * (gamma * a - beta * b))
print(str(ind) + "/" + str(N))
ims.append([imshow(a, vmin=0., vmax=1.)])
ani = ArtistAnimation(fig, ims, interval=50, blit=True, repeat_delay=1000)
Writer = writers['ffmpeg']
writer = Writer(fps=20, metadata=dict(artist='Laurent Garcin'), bitrate=18000)
ani.save('bz.mp4', writer=writer)
show()
# <codecell>
# Create an empty figure
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ims = [] # list for storing all of the created artists
# Generate a bunch of images with random data
for i in range(10):
ims.append([ax.imshow(np.random.randn(20, 20))])
# Create an animation object, passing it the figure and a
# sequence of *collections* of Artists. In this case we are
# giving it a list of lists of images.
# Frames are displayed with a 500 millisecond delay
anim = ArtistAnimation(fig, ims, interval=500)
# <markdowncell>
# Same animation, but this time delaying between restarts of the animation.
# <codecell>
from matplotlib.animation import ArtistAnimation
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ims = []
for i in range(10):
ims.append([ax.imshow(np.random.randn(20, 20))])
anim = ArtistAnimation(fig, ims, interval=500, repeat_delay=3000)
while not array_equal(access, a):
access = a.copy()
a = logical_and(binary_dilation(a), grid)
lines, colors = [], []
for i in range(N - 1):
for j in range(N):
if access[i, j] and access[i + 1, j]:
lines.append([(i, j), (i + 1, j)])
colors.append([1, 0, 0, 1])
elif grid[i, j] and grid[i + 1, j]:
lines.append([(i, j), (i + 1, j)])
colors.append([0, 0, 1, 1])
for i in range(N):
for j in range(N - 1):
if access[i, j] and access[i, j + 1]:
lines.append([(i, j), (i, j + 1)])
colors.append([1, 0, 0, 1])
elif grid[i, j] and grid[i, j + 1]:
lines.append([(i, j), (i, j + 1)])
colors.append([0, 0, 1, 1])
ax.autoscale()
ax.set_aspect('equal')
ims.append([ax.add_collection(LineCollection(lines, colors=colors))])
ani = ArtistAnimation(fig, ims, interval=10, blit=True, repeat_delay=1000)
Writer = writers['ffmpeg']
writer = Writer(fps=10, metadata=dict(artist='Laurent Garcin'), bitrate=18000)
ani.save('perco_bond.mp4', writer=writer)
show()
C = 3
N = 7
config = {
'iter': 0,
Piquet.A: list(range(N, 0, -1)),
Piquet.B: [],
Piquet.C: []
}
fig = plt.figure()
plot_config(config)
def hanoi(n, A, B, C):
if n > 0:
hanoi(n - 1, A, C, B)
config[A].pop()
config[C].append(n)
config['iter'] += 1
plot_config(config)
hanoi(n - 1, B, A, C)
hanoi(N, Piquet.A, Piquet.B, Piquet.C)
im_ani = ArtistAnimation(fig, ims, interval=50, repeat_delay=1000, blit=True)
im_ani.save('hanoi.mp4')
plt.show()
