I'd like to make some interactive plots in the Jupyter notebook, in which certain points in the plot can be dragged by the user. The locations of those points should then be used as input to a Python function (in the notebook) that updates the plot.
Something like this has been accomplished here:
http://nbviewer.ipython.org/github/maojrs/ipynotebooks/blob/master/interactive_test.ipynb
but the callbacks are to Javascript functions. In some cases, the code that updates the plot needs to be extremely complex and would take a very long time to rewrite in Javascript. I'm willing to designate the draggable points in Javascript if necessary, but is it possible to call back to Python for updating the plot?
I'm wondering if tools like Bokeh or Plotly could provide this functionality.
Have you tried bqplot? The Scatter has an enable_move parameter, that when you set to True they allow points to be dragged. Furthermore, when you drag you can observe a change in the x or y value of the Scatter or Label and trigger a python function through that, which in turn generates a new plot. They do this in the Introduction notebook.
Jupyter notebook code:
# Let's begin by importing some libraries we'll need
import numpy as np
from __future__ import print_function # So that this notebook becomes both Python 2 and Python 3 compatible
# And creating some random data
size = 10
np.random.seed(0)
x_data = np.arange(size)
y_data = np.cumsum(np.random.randn(size) * 100.0)
from bqplot import pyplot as plt
# Creating a new Figure and setting it's title
plt.figure(title='My Second Chart')
# Let's assign the scatter plot to a variable
scatter_plot = plt.scatter(x_data, y_data)
# Let's show the plot
plt.show()
# then enable modification and attach a callback function:
def foo(change):
print('This is a trait change. Foo was called by the fact that we moved the Scatter')
print('In fact, the Scatter plot sent us all the new data: ')
print('To access the data, try modifying the function and printing the data variable')
global pdata
pdata = [scatter_plot.x,scatter_plot.y]
# First, we hook up our function `foo` to the colors attribute (or Trait) of the scatter plot
scatter_plot.observe(foo, ['y','x'])
scatter_plot.enable_move = True
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