现在最简单的方法是使用 AxesGrid,就像在这个 yt 食谱示例和这个.
这是一个使用 yt 3.2.1 在时间序列中绘制两次气体密度的示例。我使用的示例数据可以从http://yt-project.org/data下载。
import yt
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
fns = ['enzo_tiny_cosmology/DD0005/DD0005', 'enzo_tiny_cosmology/DD0040/DD0040']
fig = plt.figure()
# See http://matplotlib.org/mpl_toolkits/axes_grid/api/axes_grid_api.html
# These choices of keyword arguments produce a four panel plot with a single
# shared narrow colorbar on the right hand side of the multipanel plot. Axes
# labels are drawn for all plots since we're slicing along different directions
# for each plot.
grid = AxesGrid(fig, (0.075,0.075,0.85,0.85),
nrows_ncols = (2, 1),
axes_pad = 0.05,
label_mode = "L",
share_all = True,
cbar_location="right",
cbar_mode="single",
cbar_size="3%",
cbar_pad="0%")
for i, fn in enumerate(fns):
# Load the data and create a single plot
ds = yt.load(fn) # load data
# Make a ProjectionPlot with a width of 34 comoving megaparsecs
p = yt.ProjectionPlot(ds, 'z', 'density', width=(34, 'Mpccm'))
# Ensure the colorbar limits match for all plots
p.set_zlim('density', 1e-4, 1e-2)
# This forces the ProjectionPlot to redraw itself on the AxesGrid axes.
plot = p.plots['density']
plot.figure = fig
plot.axes = grid[i].axes
plot.cax = grid.cbar_axes[i]
# Finally, this actually redraws the plot.
p._setup_plots()
plt.savefig('multiplot_1x2_time_series.png', bbox_inches='tight')
您也可以按照自己的方式进行操作(使用fig.add_subplots
代替AxesGrid
),但您需要手动定位轴并调整图形大小。
最后,如果您希望图形更小,您可以通过在创建图形时传递以英寸为单位的图形大小来控制图形的大小plt.figure()
。如果您这样做,您可能还需要通过调用p.set_font_size()
.ProjectionPlot