[英]Large matplotlib pixel figure best approach
我有一個大型 2D 數據集,我想將每個 X、Y 對關聯一個顏色,並將 plot 與 matplotlib 關聯。 我說的是1000000點。 我想知道在性能(速度)方面最好的方法是什么,如果你能指出一些例子
如果您正在處理常規網格,只需將其視為圖像:
import numpy as np
import matplotlib.pyplot as plt
nrows, ncols = 1000, 1000
z = 500 * np.random.random(nrows * ncols).reshape((nrows, ncols))
plt.imshow(z, interpolation='nearest')
plt.colorbar()
plt.show()
如果您隨機排序了組成規則網格的 x、y、z 三元組,那么您需要對它們進行網格化。
本質上,你可能有這樣的東西:
import numpy as np
import matplotlib.pyplot as plt
# Generate some data
nrows, ncols = 1000, 1000
xmin, xmax = -32.4, 42.0
ymin, ymax = 78.9, 101.3
dx = (xmax - xmin) / (ncols - 1)
dy = (ymax - ymin) / (ncols - 1)
x = np.linspace(xmin, xmax, ncols)
y = np.linspace(ymin, ymax, nrows)
x, y = np.meshgrid(x, y)
z = np.hypot(x - x.mean(), y - y.mean())
x, y, z = [item.flatten() for item in (x,y,z)]
# Scramble the order of the points so that we can't just simply reshape z
indicies = np.arange(x.size)
np.random.shuffle(indicies)
x, y, z = [item[indicies] for item in (x, y, z)]
# Up until now we've just been generating data...
# Now, x, y, and z probably represent something like you have.
# We need to make a regular grid out of our shuffled x, y, z indicies.
# To do this, we have to know the cellsize (dx & dy) that the grid is on and
# the number of rows and columns in the grid.
# First we convert our x and y positions to indicies...
idx = np.round((x - x.min()) / dx).astype(np.int)
idy = np.round((y - y.min()) / dy).astype(np.int)
# Then we make an empty 2D grid...
grid = np.zeros((nrows, ncols), dtype=np.float)
# Then we fill the grid with our values:
grid[idy, idx] = z
# And now we plot it:
plt.imshow(grid, interpolation='nearest',
extent=(x.min(), x.max(), y.max(), y.min()))
plt.colorbar()
plt.show()
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