将交互式Jupyter Notebook输出为html [英] Exporting Interactive Jupyter Notebook to html
问题描述
以下代码绘制了一个交互式图形，我可以开启/关闭特定线路。当我在Ipython Notebook中工作时，此功能非常完美。
The following code plots an interactive figure where I can toggle specific lines on/off. This works perfectly when I'm working in an Ipython Notebook
import pandas as pd
import numpy as np
from itertools import cycle
import matplotlib.pyplot as plt, mpld3
from matplotlib.widgets import CheckButtons
import matplotlib.patches
import seaborn as sns
%matplotlib nbagg
sns.set(style="whitegrid")
df = pd.DataFrame({'freq': {0: 0.01, 1: 0.02, 2: 0.029999999999999999, 3: 0.040000000000000001, 4: 0.050000000000000003, 5: 0.059999999999999998, 6: 0.070000000000000007, 7: 0.080000000000000002, 8: 0.089999999999999997, 9: 0.10000000000000001, 10: 0.01, 11: 0.02, 12: 0.029999999999999999, 13: 0.040000000000000001, 14: 0.050000000000000003, 15: 0.059999999999999998, 16: 0.070000000000000007, 17: 0.080000000000000002, 18: 0.089999999999999997, 19: 0.10000000000000001, 20: 0.01, 21: 0.02, 22: 0.029999999999999999, 23: 0.040000000000000001, 24: 0.050000000000000003, 25: 0.059999999999999998, 26: 0.070000000000000007, 27: 0.080000000000000002, 28: 0.089999999999999997, 29: 0.10000000000000001}, 'kit': {0: 'B', 1: 'B', 2: 'B', 3: 'B', 4: 'B', 5: 'B', 6: 'B', 7: 'B', 8: 'B', 9: 'B', 10: 'A', 11: 'A', 12: 'A', 13: 'A', 14: 'A', 15: 'A', 16: 'A', 17: 'A', 18: 'A', 19: 'A', 20: 'C', 21: 'C', 22: 'C', 23: 'C', 24: 'C', 25: 'C', 26: 'C', 27: 'C', 28: 'C', 29: 'C'}, 'SNS': {0: 91.198979591799997, 1: 90.263605442199989, 2: 88.818027210899999, 3: 85.671768707499993, 4: 76.23299319729999, 5: 61.0969387755, 6: 45.1530612245, 7: 36.267006802700003, 8: 33.0782312925, 9: 30.739795918400002, 10: 90.646258503400006, 11: 90.306122449, 12: 90.178571428600009, 13: 89.498299319699996, 14: 88.435374149599994, 15: 83.588435374200003, 16: 75.212585034, 17: 60.969387755100001, 18: 47.278911564600001, 19: 37.627551020399999, 20: 90.986394557800011, 21: 90.136054421799997, 22: 89.540816326499993, 23: 88.690476190499993, 24: 86.479591836799997, 25: 82.397959183699996, 26: 73.809523809499993, 27: 63.180272108800004, 28: 50.935374149700003, 29: 41.241496598699996}, 'FPR': {0: 1.0953616823100001, 1: 0.24489252678500001, 2: 0.15106142277199999, 3: 0.104478605177, 4: 0.089172822253300005, 5: 0.079856258734300009, 6: 0.065881413455800009, 7: 0.059892194050699996, 8: 0.059892194050699996, 9: 0.0578957875824, 10: 0.94097291541899997, 11: 0.208291741532, 12: 0.14773407865800001, 13: 0.107805949291, 14: 0.093165635189999998, 15: 0.082518134025399995, 16: 0.074532508152000007, 17: 0.065881413455800009, 18: 0.062554069341799995, 19: 0.061888600519100001, 20: 0.85313103081100006, 21: 0.18899314567100001, 22: 0.14107939043000001, 23: 0.110467824582, 24: 0.099820323417899995, 25: 0.085180009316599997, 26: 0.078525321088700001, 27: 0.073201570506399985, 28: 0.071870632860800004, 29: 0.0705396952153}})
tableau20 = ["#6C6C6C", "#92D050", "#FFC000"]
tableau20 = cycle(tableau20)
kits = ["A","B", "C"]
color = iter(["#6C6C6C", "#92D050", "#FFC000"])
fig = plt.figure(figsize=(12,8))
for kit in kits:
colour = next(color)
for i in df.groupby('kit'):
grouped_df = pd.DataFrame(np.array(i[1]), columns =
['freq', 'SNS', 'FPR', 'kit'])
if grouped_df.kit.tolist()[1] == kit:
x = [float(value) for i, value in enumerate(grouped_df.FPR)]
y = [float(value) for i, value in enumerate(grouped_df.SNS)]
x, y = (list(x) for x in zip(*sorted(zip(x, y))))
label = grouped_df['kit'].tolist()[1]
p = plt.plot(x, y, "-o",label = label, color = colour)
labels = [label.get_text() for label in plt.legend().texts]
plt.legend().set_visible(False)
for i, value in enumerate(labels):
exec('label%s="%s"'%(i, value))
for i in range(len(labels)):
exec('l%s=fig.axes[0].lines[i]'%(i))
rax = plt.axes([0.92, 0.7, 0.2, 0.2], frameon=False)
check = CheckButtons(rax, (labels), ('True ' * len(labels)))
for i, rec in enumerate(check.rectangles):
rec.set_facecolor(tableau20.next())
def func(label):
for i in range(len(labels)):
if label == eval('label%s'%(i)): eval('l%s.set_visible(not l%s.get_visible())'%(i,i))
plt.draw()
check.on_clicked(func)
plt.show()
问题是的，我需要将这个笔记本导出为html格式，与那些对python一无所知的同事分享。我如何将笔记本导出为html并让它保持交互（切换）功能（它目前丢失）？谢谢！
Problem is, I need to export the notebook as a html to share with colleagues who know nothing about python. How can I export the notebook to html and get it to maintain the interactive (toggle) functionality (which it currently loses)? Thanks!
推荐答案
也许您不需要将jupyter笔记本导出到html，但将笔记本链接分享给其他人以及他们可以使用他们的浏览器访问该网址。
Maybe you don't need to export jupyter notebook to html, but share the notebook link to the other people and they can visit the url using their browser.
jupyter笔记本插件可以帮助您更高效地完成此操作： jupyter / dashboards ，它由官方jupyter团队维护，它可以帮助您像报告一样共享您的笔记本，并且您可以控制显示哪个单元格以及每个单元格的位置显示。值得一试！
A jupyter notebook plugin would help you do this more efficiently: jupyter/dashboards, it's maintained by official jupyter team, and it helps you share your notebook like a report, and you can control which cell to display and the location of each cell displayed. Worth a try!
这篇关于将交互式Jupyter Notebook输出为html的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
