将 matplotlib 转换为交互式全息视图 + 数据着色器可视化（最好使用交互式画笔）

Question

如何将以下图移植到 hvplot + datashader？

理想情况下，可以保留交互性，并且可以交互地子选择某些 device_id 。 （理想情况下使用画笔，即在选择异常点时我希望能够过滤到基础系列，但如果这不起作用，也许从列表中子选择它们也可以。请记住这个列表可能很长（在 1000 个元素的区域内））。

%pylab inline
import seaborn as sns; sns.set()
import pandas as pd
from pandas import Timestamp

d = pd.DataFrame({'metrik_0': {Timestamp('2020-01-01 00:00:00'): -0.5161200349325471,
  Timestamp('2020-01-01 01:00:00'): 0.6404118012330947,
  Timestamp('2020-01-01 02:00:00'): -1.0127867504877557,
  Timestamp('2020-01-01 03:00:00'): 0.25828987625529976,
  Timestamp('2020-01-01 04:00:00'): -2.486778084008076,
  Timestamp('2020-01-01 05:00:00'): -0.30695039872663826,
  Timestamp('2020-01-01 06:00:00'): -0.6570670310316116,
  Timestamp('2020-01-01 07:00:00'): 0.3274964731894147,
  Timestamp('2020-01-01 08:00:00'): -0.8624113311084097,
  Timestamp('2020-01-01 09:00:00'): 1.0832911260447902},
 'device_id': {Timestamp('2020-01-01 00:00:00'): 9,
  Timestamp('2020-01-01 01:00:00'): 1,
  Timestamp('2020-01-01 02:00:00'): 1,
  Timestamp('2020-01-01 03:00:00'): 9,
  Timestamp('2020-01-01 04:00:00'): 9,
  Timestamp('2020-01-01 05:00:00'): 9,
  Timestamp('2020-01-01 06:00:00'): 9,
  Timestamp('2020-01-01 07:00:00'): 1,
  Timestamp('2020-01-01 08:00:00'): 1,
  Timestamp('2020-01-01 09:00:00'): 9}})

fig, ax = plt.subplots()
for dev, df in d.groupby('device_id'):
    df.plot(y='metrik_0', ax=ax, label=dev)

到目前为止，我只能实现：

import pandas as pd
import datashader as ds
import numpy as np
import holoviews as hv

from holoviews import opts

from holoviews.operation.datashader import datashade, shade, dynspread, rasterize
from holoviews.operation import decimate

hv.extension('bokeh','matplotlib')

width = 1200
height = 400
curve = hv.Curve(d)

datashade(curve, cmap=["blue"], width=width, height=height).opts(width=width, height=height)

理想情况下，我也可以突出显示类似于 matplotlib: axvspan某些范围。

Answer 1

只要你想达到100,000点左右，你就不需要Datashader：

import pandas as pd
import hvplot.pandas
from pandas import Timestamp

df = pd.DataFrame(
       {'metrik_0': {
          Timestamp('2020-01-01 00:00:00'): -0.5161200349325471,
          Timestamp('2020-01-01 01:00:00'): 0.6404118012330947,
          Timestamp('2020-01-01 02:00:00'): -1.0127867504877557,
          Timestamp('2020-01-01 03:00:00'): 0.25828987625529976,
          Timestamp('2020-01-01 04:00:00'): -2.486778084008076,
          Timestamp('2020-01-01 05:00:00'): -0.30695039872663826,
          Timestamp('2020-01-01 06:00:00'): -0.6570670310316116,
          Timestamp('2020-01-01 07:00:00'): 0.3274964731894147,
          Timestamp('2020-01-01 08:00:00'): -0.8624113311084097,
          Timestamp('2020-01-01 09:00:00'): 1.0832911260447902},
        'device_id': {
          Timestamp('2020-01-01 00:00:00'): 9,
          Timestamp('2020-01-01 01:00:00'): 1,
          Timestamp('2020-01-01 02:00:00'): 1,
          Timestamp('2020-01-01 03:00:00'): 9,
          Timestamp('2020-01-01 04:00:00'): 9,
          Timestamp('2020-01-01 05:00:00'): 9,
          Timestamp('2020-01-01 06:00:00'): 9,
          Timestamp('2020-01-01 07:00:00'): 1,
          Timestamp('2020-01-01 08:00:00'): 1,
          Timestamp('2020-01-01 09:00:00'): 9}})

df.hvplot(by='device_id')

如果你想要 vspan，你可以从 HoloViews 得到它：

import holoviews as hv
        
vspan = hv.VSpan(Timestamp('2020-01-01 04:00:00'),
                 Timestamp('2020-01-01 06:00:00'))
                 
df.hvplot(by='device_id') * vspan.opts(color='red')

如果你确实想要 Datashader，你可以拥有它，但如果不进一步工作，结果将无法选择：

df.hvplot(by='device_id', datashade=True, dynspread=True) * vspan.opts(color='red')