更改 Holoviews/Datashader 中分类数据的颜色图

Question

我正在尝试使用 Datashader 和 Holoviews 可视化分类空间数据，类似于 https://anaconda.org/jbednar/census-hv-dask/notebook. However, when I try to assign different colors to categories, I always end up with same (presumably default) colors (An example of the output image。）

这是我在 Jupyter notebook 中运行ning 的代码。谁能告诉我如何使自定义颜色图起作用？或者至少运行代码以查看最终颜色是否与图例匹配。谢谢！

from sklearn.datasets.samples_generator import make_blobs
from matplotlib import pyplot
import pandas as pd

import holoviews as hv
import geoviews as gv
import datashader as ds
from cartopy import crs
from matplotlib.cm import get_cmap
from holoviews.operation.datashader import datashade, aggregate
hv.notebook_extension('bokeh', width=95)

# Generating blob data:
X, y = make_blobs(n_samples=5000000, centers=5, n_features=2)
df = pd.DataFrame(dict(x=X[:,0], y=X[:,1], label=y))

# Plotting the blobs using datashader and holoviews:
%opts Overlay [width=800 height=455 xaxis=None yaxis=None show_grid=False] 
%opts Shape (fill_color=None line_width=1.5) [apply_ranges=False] 
%opts Points [apply_ranges=False] WMTS (alpha=0.5) NdOverlay [tools=['tap']]

color_key = {0:'red', 1:'blue', 2:'green', 3:'yellow', 4:'black'}
labels    = {0:'red', 1:'blue', 2:'green', 3:'yellow', 4:'black'}

color_points = hv.NdOverlay({labels[k]: gv.Points([0,0], crs=crs.PlateCarree(),
                            label=labels[k])(style=dict(color=v))
                            for k, v in color_key.items()})

dataset = gv.Dataset(df, kdims=['x', 'y'], vdims=['label'])
shaded = datashade(hv.Points(dataset), cmap=color_key, aggregator=ds.count_cat('label'))

shaded * color_points

Answer 1

该代码似乎不可运行（未定义种族，未导入 gv），但无论如何，分类颜色由 color_key 参数决定，而不是 cmap，因此您需要将 cmap=color_key 更改为 color_key=color_key。

更改 Holoviews/Datashader 中分类数据的颜色图

Changing colormap for categorical data in Holoviews/Datashader

python

colormap

holoviews

datashader