def save_frame(idx):
w = z * (1.0 - interp[idx]) + np.tan(1 / z) * (interp[idx])
cfunc_plot = dcoloring.colorize(w, grid=False)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(cfunc_plot,
extent=(-WIDTH / 2, WIDTH / 2, -HEIGHT / 2, HEIGHT / 2))
ax.set(xlabel='{:3.0f}%'.format(interp[idx] * 100), title=func_name)
# Save frame
print('Rendering frame {0:{2}}/{1:{2}}'.format(idx, FRAMES,
int(np.log10(FRAMES) + 1)))
temp_path = '{}/frame.{}.png'.format(TEMP_DIR, idx)
fig.savefig(temp_path, dpi=600, transparent=True)
# Resize for aliasing
img = cv2.imread(temp_path)
img = cv2.resize(img, (int(img.shape[1] / 3), int(img.shape[0] / 3)),
interpolation=cv2.INTER_AREA)
cv2.imwrite(temp_path, img)
# Save image of complete function
if idx == FRAMES - 1:
img_path = '{}.png'.format(path)
fig.savefig(img_path, dpi=1600, transparent=True)
img = cv2.imread(img_path)
img = cv2.resize(img, (int(img.shape[1] / 4), int(img.shape[0] / 4)),
interpolation=cv2.INTER_AREA)
cv2.imwrite('{}.png'.format(path), img)
plt.close(fig)
import cv2
WIDTH = 12
HEIGHT = 24
HEIGHT_OFFSET = 11
POINTS_PER_DIM = 2048
x, y = np.ogrid[-WIDTH / 2:WIDTH / 2:POINTS_PER_DIM / 2 * 1j, HEIGHT_OFFSET +
(-HEIGHT / 2):HEIGHT_OFFSET + (HEIGHT / 2):POINTS_PER_DIM * 1j]
fig = plt.figure()
ax = fig.add_subplot(111)
z = x + 1j * y
z_img = dcoloring.colorize(np.vectorize(dcoloring.zeta)(z, E=1e-9), grid=False)
ax.set(title='f(z)')
ax.imshow(z_img,
extent=(-WIDTH / 2, WIDTH / 2, HEIGHT_OFFSET + (-HEIGHT / 2),
HEIGHT_OFFSET + (HEIGHT / 2)))
path = 'f(z).png'
fig.savefig(path, dpi=1600, transparent=True)
# Resize for aliasing
img = cv2.imread(path)
img = cv2.resize(img, (int(img.shape[1] / 4), int(img.shape[0] / 4)),
interpolation=cv2.INTER_AREA)
cv2.imwrite(path, img)
bias2 = dcoloring.clover(2 * t)
weight3 = dcoloring.clover(3 * t)
bias3 = dcoloring.clover(3 * t, -np.pi / 2)
#This loop goes over each weight and bias and generates a plot for each
#These plots are aggregated to make a video showcasing some of the function space
file_names = []
fig = plt.figure()
ax = fig.add_subplot(111)
for idx in range(0, len(t)):
w = np.exp(weight1[idx] * np.log(weight2[idx] * np.exp(z) + bias2[idx]) +
bias1[idx])
img = dcoloring.colorize(w, grid=False)
ax.clear()
ax.imshow(img, extent=(-WIDTH / 2, WIDTH / 2, -HEIGHT / 2, HEIGHT / 2))
ax.set_xlim(-WIDTH / 2, WIDTH / 2)
ax.set_ylim(-HEIGHT / 2, HEIGHT / 2)
ax.set(xlabel='{}'.format(idx))
print('Rendering frame {0:{2}}/{1:{2}}'.format(idx, FRAMES,
int(np.log10(FRAMES) + 1)))
fig.savefig('{}/frame.{}.png'.format(TEMP_DIR, idx))
file_names.append('{}/frame.{}.png'.format(TEMP_DIR, idx))
render.create_webm('graph', file_names)
rmtree(TEMP_DIR)
fig = plt.figure()
ax = fig.add_subplot(111)
# Set interpolation and animate transition between two complex functions
file_names = []
path = OUT_DIR + '/' + func_name
if not os.path.exists(TEMP_DIR):
os.makedirs(TEMP_DIR)
interp = cerp
for idx in range(0, len(interp)):
w = z * (1.0 - interp[idx]) + cfunction * (interp[idx])
cfunc_plot = dcoloring.colorize(w, grid=False, log_base=log_base)
ax.clear()
ax.imshow(cfunc_plot, extent=(-WIDTH/2, WIDTH/2, HEIGHT_OFFSET + (-HEIGHT / 2), HEIGHT_OFFSET + (HEIGHT / 2)))
ax.set(xlabel='{:3.0f}%'.format(round(interp[idx] * 100)), title=func_name)
# Save frame
print('Rendering frame {0:{2}}/{1:{2}}'.format(idx + 1, FRAMES, int(np.log10(FRAMES) + 1)))
temp_path = '{}/frame.{}.png'.format(TEMP_DIR, idx)
fig.savefig(temp_path, dpi=1000, transparent=True)
file_names.append(temp_path)
# Resize for aliasing
img = cv2.imread(temp_path)
img = cv2.resize(img, (int(img.shape[1]/8), int(img.shape[0]/8)), interpolation=cv2.INTER_AREA)
cv2.imwrite(temp_path, img)
fig = plt.figure()
ax = fig.add_subplot(111)
# Set interpolation and animate transition between two complex functions
file_names = []
path = OUT_DIR + '/' + func_name
if not os.path.exists(TEMP_DIR):
os.makedirs(TEMP_DIR)
interp = cerp
for idx in range(0, len(interp)):
w = z * (1.0 - interp[idx]) + cfunction * (interp[idx])
cfunc_plot = dcoloring.colorize(w, grid=False)
ax.clear()
ax.imshow(cfunc_plot, extent=(-WIDTH/2, WIDTH/2, -HEIGHT/2, HEIGHT/2))
ax.set(xlabel='{:3.0f}%'.format(1.0 * 100), title=func_name)
# Save frame
print('Rendering frame {0:{2}}/{1:{2}}'.format(idx + 1, FRAMES, int(np.log10(FRAMES) + 1)))
temp_path = '{}/frame.{}.png'.format(TEMP_DIR, idx)
fig.savefig(temp_path, dpi=1600, transparent=True)
file_names.append(temp_path)
# Resize for aliasing
img = cv2.imread(temp_path)
img = cv2.resize(img, (int(img.shape[1]/4), int(img.shape[0]/4)), interpolation=cv2.INTER_AREA)
cv2.imwrite(temp_path, img)
import numpy as np
import dcoloring
import cv2
WIDTH = 16.0
HEIGHT = 16.0
POINTS_PER_DIM = 2048
x, y = np.ogrid[
-WIDTH/2:WIDTH/2:POINTS_PER_DIM*1j,
-HEIGHT/2:HEIGHT/2:POINTS_PER_DIM*1j
]
fig = plt.figure()
ax = fig.add_subplot(111)
z = x + 1j*y
z_img = dcoloring.colorize(z, grid=False)
ax.set(title='z')
ax.imshow(z_img, extent=(-WIDTH/2,WIDTH/2,-HEIGHT/2,HEIGHT/2))
path = 'z.png'
fig.savefig(path, dpi=1600, transparent=True)
# Resize for aliasing
img = cv2.imread(path)
img = cv2.resize(img, (int(img.shape[1]/4), int(img.shape[0]/4)), interpolation=cv2.INTER_AREA)
cv2.imwrite(path, img)