va4.ax.set_title(f'a[:,None,:] * b of shape=(2, 3, 3)')
va4.set_colors(va4.get_indices().T, color='lightgreen', basecolor='aqua')
va4.vizualize(fixview=True, usetex=False, scale=1.2)
va4.ax.dist = 12
ax = fig.add_subplot(2, 3, 5, projection='3d')
arr = np.sum((a[:, None, :] * b), axis=2)
va5 = VisualArray(arr, fig=fig, ax=ax)
va5.ax.set_title(f'np.sum((a[:,None,:] * b), axis=2) \n of shape=(2, 3)')
va5.set_colors(va5.get_indices().T, color='lightgreen', basecolor='aqua')
va5.vizualize(fixview=True,
axis_labels=(None, 'axis=0', 'axis=1'),
usetex=False,
scale=0.8)
va5.ax.dist = 12
ax = fig.add_subplot(2, 3, 6, projection='3d')
arr = np.around(np.sum((a[:, None, :] * b), axis=2) / c, 1)
va6 = VisualArray(arr, fig=fig, ax=ax)
va6.arr = np.array([[[format(n, '.10g') for n in m] for m in o]
for o in va6.arr])
va6.ax.set_title(f'np.sum((a[:,None,:] * b), axis=2)/c \n of shape=(2, 3)')
va6.set_colors(va6.get_indices().T, color='lightgreen', basecolor='aqua')
va6.vizualize(fixview=True,
axis_labels=(None, 'axis=0', 'axis=1'),
usetex=False,
scale=0.7)
va6.ax.dist = 12
plt.get_current_fig_manager().window.state('zoomed')
plt.show()
arr = np.asarray(arr, dtype='uint32')
hexarr = np.vectorize(tohexarr)
return hexarr((arr[:, :, 0] << 16) + (arr[:, :, 1] << 8) + arr[:, :, 2])
def tolabels(arr):
def tolabelarr(x):
return r'\begin{array}{l}\,\,\sharp ' + x[1:4] + r'\\ \,\,\,\, ' + x[
4:7] + r'\\ ' + r'\\ ' + r'\\ ' + r'\end{array}'
labelarr = np.vectorize(tolabelarr)
return labelarr(arr)
from PIL import Image
test_image = Image.open('cat.jpg')
test_image = test_image.resize((32, 32), Image.ANTIALIAS)
test_image = np.array(test_image).astype(int)
arr = tohex(test_image)
va = VisualArray(arr)
cells = va.get_indices()
x, y, z = cells.T
va.set_colors(cells.T, color=va.arr[x, y, z])
va.arr = tolabels(va.arr)
va.vizualize(fixview=True, scale=0.35, axis_labels=(None, None, None))
va.ax.dist = 11.5 #zoom out a little; change to 3.5 for higher zoom
plt.get_current_fig_manager().window.state('zoomed')
plt.show()