如何对齐数据点和刻度标签?
How to align data points and tick labels?
我想绘制一个带圆圈的网格并标记每一行和每一列。虽然我可以绘制数据点,但我无法正确对齐数据点及其各自的标签,因此绘图如下所示:
如何正确完成,使刻度标签与数据点对齐?
代码:
import numpy as np
import itertools
from bokeh.models import Circle, ColumnDataSource
from bokeh.plotting import figure, show
space = 0.5
row_ind = np.arange(0, 2, space)
col_ind = np.arange(0, 1.5, space)
data_points = list(itertools.product(row_ind, col_ind))
rows = [dp[0] for dp in data_points]
cols = [dp[1] for dp in data_points]
size = 10
sizes = [size] * len(data_points)
source = ColumnDataSource(dict(columns=cols, rows=rows, size=sizes))
plot = figure(
plot_width=400,
plot_height=300,
x_range=["1", "2", "3"],
y_range=list("ABCD")
)
plot.circle(
x="columns",
y="rows",
size="size",
line_color="black",
fill_color="white",
line_width=2,
source=source
)
show(plot)
您应该使用散景的默认范围,然后使用 plot.xaxis.ticker
、plot.yaxis.ticker
和 plot.yaxis.major_label_overrides
更改刻度。
完整示例
import numpy as np
import itertools
from bokeh.models import Circle, ColumnDataSource
from bokeh.plotting import figure, show, output_notebook
output_notebook()
row_ind = np.arange(0, 2, 0.5)
col_ind = np.arange(0, 1.5, 0.5)
data_points = list(itertools.product(row_ind, col_ind))
rows = [dp[0] for dp in data_points]
cols = [dp[1] for dp in data_points]
sizes = [10] * len(data_points)
source = ColumnDataSource(dict(columns=cols, rows=rows, size=sizes))
plot = figure(
plot_width=400,
plot_height=300
)
plot.circle(
x="columns",
y="rows",
size="size",
line_color="black",
fill_color="white",
line_width=2,
source=source
)
plot.xaxis.ticker, plot.yaxis.ticker = col_ind, row_ind
plot.yaxis.major_label_overrides = dict(zip(
[int(i) if i.is_integer() else i for i in row_ind],
list('ABCD'))
)
show(plot)
输出
我想绘制一个带圆圈的网格并标记每一行和每一列。虽然我可以绘制数据点,但我无法正确对齐数据点及其各自的标签,因此绘图如下所示:
如何正确完成,使刻度标签与数据点对齐?
代码:
import numpy as np
import itertools
from bokeh.models import Circle, ColumnDataSource
from bokeh.plotting import figure, show
space = 0.5
row_ind = np.arange(0, 2, space)
col_ind = np.arange(0, 1.5, space)
data_points = list(itertools.product(row_ind, col_ind))
rows = [dp[0] for dp in data_points]
cols = [dp[1] for dp in data_points]
size = 10
sizes = [size] * len(data_points)
source = ColumnDataSource(dict(columns=cols, rows=rows, size=sizes))
plot = figure(
plot_width=400,
plot_height=300,
x_range=["1", "2", "3"],
y_range=list("ABCD")
)
plot.circle(
x="columns",
y="rows",
size="size",
line_color="black",
fill_color="white",
line_width=2,
source=source
)
show(plot)
您应该使用散景的默认范围,然后使用 plot.xaxis.ticker
、plot.yaxis.ticker
和 plot.yaxis.major_label_overrides
更改刻度。
完整示例
import numpy as np
import itertools
from bokeh.models import Circle, ColumnDataSource
from bokeh.plotting import figure, show, output_notebook
output_notebook()
row_ind = np.arange(0, 2, 0.5)
col_ind = np.arange(0, 1.5, 0.5)
data_points = list(itertools.product(row_ind, col_ind))
rows = [dp[0] for dp in data_points]
cols = [dp[1] for dp in data_points]
sizes = [10] * len(data_points)
source = ColumnDataSource(dict(columns=cols, rows=rows, size=sizes))
plot = figure(
plot_width=400,
plot_height=300
)
plot.circle(
x="columns",
y="rows",
size="size",
line_color="black",
fill_color="white",
line_width=2,
source=source
)
plot.xaxis.ticker, plot.yaxis.ticker = col_ind, row_ind
plot.yaxis.major_label_overrides = dict(zip(
[int(i) if i.is_integer() else i for i in row_ind],
list('ABCD'))
)
show(plot)
输出