python-3.x - Holoviews(Bokeh) 图的辅助轴

Question

我正在尝试绘制一组图表，上面覆盖有显示它们之间差异百分比的图表。

我用于绘图的代码:

%%output size = 200
%%opts Curve[height=200, width=400,show_grid=True,tools=['hover','box_select'], xrotation=90]
%%opts Curve(line_width=1)


from bokeh.models import Range1d, LinearAxis

new_df = new_df.astype('float')
percent_diff_df = percent_diff_df.astype('float')

def twinx(plot, element):
    # Setting the second y axis range name and range
    start, end = (element.range(1))
    label = element.dimensions()[1].pprint_label
    plot.state.extra_y_ranges = {"foo": Range1d(start=0, end=150)}
    # Adding the second axis to the plot. 
    linaxis = LinearAxis(axis_label='% Difference', y_range_name='foo')
    plot.state.add_layout(linaxis, 'right')

wavelength = hv.Dimension('wavelength', label = 'Wavelength', unit = 'nm')
radiance = hv.Dimension('radiance', label = 'Radiance', unit = 'W/m^2/sr/nm')
curve = hv.Curve((new_df['Wave'], new_df['Level_9']), wavelength, radiance,label = 'Level_9', group = 'Requirements')*\
    hv.Curve((new_df['gcal_wave'],new_df['gcal_9']),wavelength, radiance,label = 'GCAL_9', group = 'Requirements')
curve2 = hv.Curve((percent_diff_df['pdiff_wave'],percent_diff_df['pdiff_9']), label = '% Difference', group = 'Percentage Difference').opts(plot=dict(finalize_hooks=[twinx]), style=dict(color='purple'))
curve * curve2

结果如下所示:


扁平的蓝色图表实际上应该是这样的:


我需要在两个尺度上绘制这两个图表。我似乎能够添加比例但不能将任何地 block 附加到比例上。

Answer 1

您的解决方案几乎完成。要实际使用您在钩子(Hook)中创建的范围和 Axis ，您需要访问底层 Bokeh 字形并设置其 y_range_name。

一般示例如下所示:

import pandas as pd
import holoviews as hv
from bokeh.models.renderers import GlyphRenderer

hv.extension('bokeh')

def apply_formatter(plot, element):

    p = plot.state

    # create secondary range and axis
    p.extra_y_ranges = {"twiny": Range1d(start=0, end=35)}
    p.add_layout(LinearAxis(y_range_name="twiny"), 'right')

    # set glyph y_range_name to the one we've just created
    glyph = p.select(dict(type=GlyphRenderer))[0]
    glyph.y_range_name = 'twiny'

dts = pd.date_range('2015-01-01', end='2015-01-10').values

c_def = hv.Curve((dts, np.arange(10)), name='default_axis').options(color='red', width=300)
c_sec = hv.Curve((dts, np.arange(10)), name='secondary_axis').options(color='blue',width=300, hooks=[apply_formatter])
c_def + c_def * c_sec + c_sec

有关详细信息，请参阅此处的原始 github 问题: https://github.com/pyviz/holoviews/issues/396

已编辑 - 高级示例

自从原始问题得到回答以来已经有一段时间了，随着 github 上的线程的推进，我想交叉发布一个更高级的示例，以处理原始答案中显示的一些不一致之处。我仍然鼓励您通过 github 上的线程来挖掘细节，因为全息 View 中的多 Axis 图目前需要了解 Bokeh 如何处理多 Axis 。

import pandas as pd
import streamz
import streamz.dataframe
import holoviews as hv
from holoviews import opts
from holoviews.streams import Buffer
from bokeh.models import Range1d, LinearAxis

hv.extension('bokeh')

def plot_secondary(plot, element):
    ''' 
    A hook to put data on secondary axis
    '''
    p = plot.state

    # create secondary range and axis
    if 'twiny' not in [t for t in p.extra_y_ranges]:
        # you need to manually recreate primary axis to avoid weird behavior if you are going to 
        # use secondary_axis in your plots. From what i know this also relates to the way axis
        # behave in bokeh and unfortunately cannot be modified from hv unless you are 
        # willing to rewrite quite a bit of code
        p.y_range = Range1d(start=0, end=10)
        p.y_range.name = 'default'
        p.extra_y_ranges = {"twiny": Range1d(start=0, end=10)}
        p.add_layout(LinearAxis(y_range_name="twiny"), 'right')

    # set glyph y_range_name to the one we've just created
    glyph = p.renderers[-1]
    glyph.y_range_name = 'twiny'

    # set proper range
    glyph = p.renderers[-1]
    vals = glyph.data_source.data['y'] # ugly hardcoded solution, see notes below
    p.extra_y_ranges["twiny"].start = vals.min()* 0.99
    p.extra_y_ranges["twiny"].end = vals.max()* 1.01

# define two streamz random dfs to sim data for primary and secondary plots
simple_sdf = streamz.dataframe.Random(freq='10ms', interval='100ms')
secondary_sdf = streamz.dataframe.Random(freq='10ms', interval='100ms')

# do some transformation
pdf = (simple_sdf-0.5).cumsum()
sdf = (secondary_sdf-0.5).cumsum()

# create streams for holoviews from these dfs
prim_stream = Buffer(pdf.y)
sec_stream = Buffer(sdf.y)

# create dynamic maps to plot streaming data
primary = hv.DynamicMap(hv.Curve, streams=[prim_stream]).opts(width=400, show_grid=True, framewise=True)
secondary = hv.DynamicMap(hv.Curve, streams=[sec_stream]).opts(width=400, color='red', show_grid=True, framewise=True, hooks=[plot_secondary])
secondary_2 = hv.DynamicMap(hv.Curve, streams=[prim_stream]).opts(width=400, color='yellow', show_grid=True, framewise=True, hooks=[plot_secondary])

# plot these maps on the same figure
primary * secondary * secondary_2