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

如何将以下绘图移植到 hvplot + datashader？

理想情况下，可以保留交互性并且某些 device_id 可以交互地被选中 。（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底层系列，但如果这不起作用，也许从列表中选择它们也可以。请记住这个列表可能相当长（在1000个元素的范围内））。

%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，我可以突出显示类似于 matplotlib 的某些范围：axvspan 。

Answer 1

只要你想要最多100,000点左右，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 获得：

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')

如果您确实需要 Datashader，您可以拥有它，但如果不进行进一步的工作，将无法选择结果：

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

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）

convert matplotlib to interactive holoviews + datashader visualization (ideally with interactive brush)

python

time-series

seaborn

datashader

hvplot