Bokeh image() 繪圖有什么問題？ 它成功但沒有顯示圖表

Question

我最初有一個 Spark dataframe 的數據如下：

+-------------------+--------------+------+-----+
|window_time        |delayWindowEnd|values|index|
+-------------------+--------------+------+-----+
|2022-01-24 18:00:00|999           |999   |2    |
|2022-01-24 19:00:00|999           |999   |1    |
|2022-01-24 20:00:00|999           |999   |3    |
|2022-01-24 21:00:00|999           |999   |4    |
|2022-01-24 22:00:00|999           |999   |5    |
|2022-01-24 18:00:00|998           |998   |4    |
|2022-01-24 19:00:00|998           |998   |5    |
|2022-01-24 20:00:00|998           |998   |3    |

我想 plot 作為熱圖，在 Apache Zeppelin 中使用以下代碼：

%spark.pyspark

import bkzep
import numpy as np
from bokeh.io import output_notebook, show
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, ColorBar, LogColorMapper
from bokeh.layouts import gridplot
from pyspark.sql.functions import col, coalesce, lit, monotonically_increasing_id
from pyspark.sql import DataFrame
from pyspark.sql.functions import *

output_notebook(notebook_type='zeppelin')

然后

%pyspark

從 pyspark.sql.functions 導入 *

def plot_summaries(sensor, dfName):
    df = sqlContext.table(dfName)
    pdf = df.toPandas()
    source = ColumnDataSource(pdf)

    color_mapper = LogColorMapper(palette="Viridis256", low=1, high=10)

    plot = figure(toolbar_location=None,x_axis_type='datetime')
    plot.image(x='window_time', y='delayWindowEnd', source=source, image='index',dw=1,dh=1,  color_mapper=color_mapper)

    color_bar = ColorBar(color_mapper=color_mapper, label_standoff=12)

    plot.add_layout(color_bar, 'right')
    show(gridplot([plot], ncols=1, plot_width=1000, plot_height=400))

sensors = [   
"all"
]

然后最后

%pyspark

from pyspark.sql.functions import *

keyCol = "month_day_hour"

sensors = [
    "all"]


for sensor in sensors:
    plot_summaries(sensor, "maxmin2")   

最新的已經成功，但我沒有看到圖表。

這可能是因為參數濫用。

是否可以使用 dataframe 列作為圖像參數（而其他兩個將是 x 和 y 軸）。 df 和 dw 是否正確初始化？ 可以讓 X 軸作為時間戳嗎？

如果原因是瀏覽器渲染，會出現如下 JS 錯誤：

polyfills.d42c9551b0788083cd69.js:1 Uncaught Error: Error rendering Bokeh model: could not find #fb19be38-e25a-4ebf-a488-593cd2e9a4d6 HTML tag
    at o (bokeh-1.3.4.min.js:31:143801)
    at Object.n._resolve_root_elements (bokeh-1.3.4.min.js:31:144274)
    at Object.n.embed_items_notebook (bokeh-1.3.4.min.js:31:147281)
    at embed_document (<anonymous>:6:20)
    at <anonymous>:15:9
    at e.invokeTask (polyfills.d42c9551b0788083cd69.js:1:8063)
    at t.runTask (polyfills.d42c9551b0788083cd69.js:1:3241)
    at t.invokeTask (polyfills.d42c9551b0788083cd69.js:1:9170)
    at i.useG.invoke (polyfills.d42c9551b0788083cd69.js:1:9061)
    at n.args.<computed> (polyfills.d42c9551b0788083cd69.js:1:38948)

雖然 Zeppelin 后端對執行和繪圖結果的響應通過 websocket 應用程序到達瀏覽器，但看起來很漂亮而且相當正確：

https://pastebin.com/pLWBA8Cv

Answer 1

答案在這里給出： https://discourse.bokeh.org/t/cant-render-heatmap-data-for-apache-zeppelins-pyspark-dataframe/8844

錯誤的解釋在上面的鏈接中有詳細解釋。 不久，我沒有將所需的 2D 陣列應用於散景，我不得不使用pandas ' pivot和numpy來制作它。 這是解決方案：

dft = sqlContext.table(dfName)
pdf = dft.toPandas()
import pandas as pd
rowIDs = pdf['values']
colIDs = pdf['window_time']

A = pdf.pivot_table('index', 'values', 'window_time', fill_value=0)
source = ColumnDataSource(data={'x':[pd.to_datetime('Jan 24 2022')] #left most
                           ,'y':[0] #bottom most 
                           ,'dw':[pdf['window_time'].max()-pdf['window_time'].min()] #TOTAL width of image
                           #,'dh':[df['delayWindowEnd'].max()] #TOTAL height of image
                           ,'dh':[1000] #TOTAL height of image
                           ,'im':[A.to_numpy()] #2D array using to_numpy() method on pivotted df
                           })

color_mapper = LogColorMapper(palette="Viridis256", low=1, high=20)

plot = figure(toolbar_location=None,x_axis_type='datetime')
plot.image(x='x', y='y', source=source, image='im',dw='dw',dh='dh',  color_mapper=color_mapper)

color_bar = ColorBar(color_mapper=color_mapper, label_standoff=12)

plot.add_layout(color_bar, 'right')
show(gridplot([plot], ncols=1, plot_width=1000, plot_height=400)) 

結果對我來說很漂亮：