Question

我正在尝试将数据阴影图放在我正在处理的散景服务器仪表板中。它主要起作用，除非我尝试放大绘图的y轴时它将“重置”。我设法制作了这个脚本，它可以重现我遇到的问题。我的相关环境是：

bokeh = 0.12.11
datashader = 0.6.3
holoviews = 1.9.1-x-gc1d66c015

以下是我使用bokeh serve test.py投放的代码。我也在远程计算机上运行它，并使用散点服务器文档中描述的SSH隧道。几乎相同的代码在jupyter笔记本中运行良好，在同一台机器上运行。所以我怀疑它是散景服务器中缺少的问题或配置。

from bokeh.io import curdoc
import holoviews as hv
import os
import pandas as pd
import numpy as np
import datashader as ds
from bokeh.models import DatetimeTickFormatter
from bokeh.palettes import Category20
from holoviews.operation.datashader import datashade, dynspread
import holoviews.plotting.bokeh  # noqa (Activate backend)

def read_data():
    t = pd.date_range(start='2017-01-01', freq='1T', periods=1e6)
    x = np.arange(t.size)
    y1 = np.cos(x / 100)
    y2 = 10 + np.sin(x / 100)

    classes = list(range(1, 15))
    class_list = []
    for c in classes:
        class_list += [c] * 100
        cluster = class_list * (t.size // 100 + 1)
    df = pd.DataFrame(data=dict(cluster=cluster[:t.size],
                                T1C1=y1, T1C2=y2),
                      index=t)
    df.index.name = 'time'
    return df

df = read_data()

# Convert index to integer so datashader will work correctly.
df.index = df.index.values.astype(np.int64) / 1e6
class_names = [str(c) for c in sorted(df['cluster'].unique()) if int(c) >= 0]

def apply_formatter(plot, element):
         plot.handles['xaxis'].formatter = DatetimeTickFormatter()

def get_points_data(feature):
    d = {}
    df.index.name = 'time'
    for cluster_num in class_names:
        subset = df.loc[df['cluster'] == int(cluster_num), feature].dropna()
        d[cluster_num] = hv.Points(data=subset.reset_index(), kdims=['time', feature])
    return hv.NdOverlay(d, kdims='k')

spec = {'plot': {'RGB': {'width': 800, 'height': 300,
                              'finalize_hooks': [apply_formatter],
                              # 'tools': 'pan,box_zoom,wheel_zoom,save,hover'
                              }
                      },
             'norm': {'RGB': {'framewise': True}}}

n_clusters = df['cluster'].nunique()
if n_clusters <= 20:
    colors = Category20[n_clusters]
else:
    num_repeats = n_clusters // 20 + 1
    colors = Category20[20] * num_repeats
    colors = colors[:n_clusters]

renderer = hv.Store.renderers['bokeh'].instance(mode='server', holomap='server')
DataSelector = hv.streams.Stream.define('feature', feature='T1C1')
data_stream = DataSelector(feature='T1C1')
dmap_points = hv.DynamicMap(get_points_data, streams=[data_stream])
rangexy = hv.streams.RangeXY(transient=True)
plotsize = hv.streams.PlotSize()
datashaded_points = dynspread(datashade(dmap_points, color_key=colors,
                                             streams=[rangexy, plotsize],
                                             aggregator=ds.count_cat('k'),
                                             min_alpha=0.1
                                             ), threshold=0.5, max_px=10).opts(spec)


renderer = hv.Store.renderers['bokeh'].instance(mode='server', holomap='server')
doc = curdoc()
plot = renderer.get_plot(datashaded_points, doc=doc)

doc.add_root(plot.state)

散景服务器中的holoviews + datashader在缩放时重置

0 个答案: