"""this example demonstrates how to image 3D numpy array
indices are colored in yellow if their sum is divisible by 3"""
import numpy as np
import matplotlib.pyplot as plt
from numpyviz import VisualArray
A = np.array([[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]])
B = np.array([[[2, 3, 0], [3, 1, 2]]])
fig = plt.figure()
ax = fig.add_subplot(1, 3, 1, projection='3d')
va = VisualArray(A, fig=fig, ax=ax)
coords = va.get_indices()
va.set_colors(coords.T, color='yellow', basecolor='lightblue')
va.vizualize(fixview=True)
va.ax.set_title('A = array of shape (4,2,3)')
va.ax.dist = 12.5
ax = fig.add_subplot(1, 3, 2, projection='3d')
va = VisualArray(B, fig=fig, ax=ax)
coords = va.get_indices()
va.set_colors(coords.T, color='yellow', basecolor='lightblue')
va.vizualize(fixview=True)
va.ax.set_title('B = array of shape (1,2,3)')
va.ax.dist = 12.5
np.put_along_axis(A, B, 1, 0)
ax = fig.add_subplot(1, 3, 3, projection='3d')
import numpy as np
import matplotlib.pyplot as plt
from numpyviz import VisualArray
v = np.array([[2, 5, 3, 5, 1, 8], [4, 6, 2, 7, 5, 9], [1, 8, 2, 3, 1, 4],
[2, 8, 1, 4, 3, 5], [5, 7, 2, 3, 7, 8], [1, 2, 4, 6, 3, 5],
[3, 5, 2, 8, 1, 4]])
s = np.sort(v, axis=1)
arr1 = v[(s[:, :-1] != s[:, 1:]).all(1)]
arr2 = arr1.reshape(-1, 3, 2)
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1, projection='3d')
ax.set_title('Initial array')
va = VisualArray(arr1, fig=fig, ax=ax)
va.set_colors(va.get_indices().T, color='yellow', basecolor='lightblue')
va.vizualize(fixview=True, axis_labels=(None, 'axis=0', 'axis=1'))
ax.dist = 12
ax = fig.add_subplot(1, 2, 2, projection='3d')
ax.set_title('Reshaped array')
va = VisualArray(arr2, fig=fig, ax=ax)
va.set_colors(va.get_indices().T, color='yellow', basecolor='lightblue')
va.vizualize(fixview=True)
ax.dist = 12
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from numpyviz import VisualArray
a = np.array([[1, 2, 3], [3, 4, 5]])
b = np.array([[[1, 0, 1], [1, 1, 0], [0, 1, 1]],
[[1, 1, 1], [0, 1, 0], [0, 0, 1]]])
c = np.array([1, 2, 3])
fig = plt.figure()
ax = fig.add_subplot(2, 3, 1, projection='3d')
va1 = VisualArray(a[:, None, :], fig=fig, ax=ax)
va1.ax.set_title(f'a[:,None,:] of shape=(2, 1, 3)')
va1.set_colors(va1.get_indices().T, color='lightgreen', basecolor='aqua')
va1.vizualize(fixview=True, usetex=False, scale=1.2)
va1.ax.dist = 12
ax = fig.add_subplot(2, 3, 2, projection='3d')
va2 = VisualArray(b, fig=fig, ax=ax)
va2.ax.set_title(f'b of shape=(2, 3, 3)')
va2.set_colors(va2.get_indices().T, color='lightgreen', basecolor='aqua')
va2.vizualize(fixview=True, usetex=False, scale=1.2)
va2.ax.dist = 12
ax = fig.add_subplot(2, 3, 3, projection='3d')
va3 = VisualArray(c, fig=fig, ax=ax)
va3.ax.set_title(f'c of shape=(3,)')
va3.set_colors(va3.get_indices().T, color='lightgreen', basecolor='aqua')
va3.vizualize(fixview=True,
import numpy as np
import matplotlib.pyplot as plt
from numpyviz import VisualArray
import matplotlib.gridspec as gridspec
arr = np.random.randint(99, size=24).reshape((3, 4, 2))
fig = plt.figure(constrained_layout=True)
spec = gridspec.GridSpec(ncols=5, nrows=1, figure=fig)
ax = fig.add_subplot(spec[0], projection='3d')
ax.set_title(f'body of shape={arr.shape}')
va = VisualArray(arr, fig=fig, ax=ax)
va.set_colors(va.get_indices().T, color='lawngreen', basecolor='aqua')
va.mix_colors(va.get_indices_chequerwise((1, 1, arr.shape[2])).T, 'black')
va.vizualize(fixview=True, axis_labels=('axis=0', 'axis=1', 'axis=2'))
ax = fig.add_subplot(spec[1:], projection='3d')
ax.set_title(f'body of shape={arr.flatten().shape}')
va2 = VisualArray(va.arr.flatten(), fig=fig, ax=ax)
va2.set_colors(va2.get_indices().T, color='lawngreen', basecolor='aqua')
va2.mix_colors(va2.get_indices_chequerwise((1, 1, arr.shape[2])).T, 'black')
va2.vizualize(fixview=True, axis_labels=(None, None, 'axis=0'))
ax.dist = 8
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()