The first line simply reloads matplotlib, uses the nbagg backend and then reloads the backend, just to ensure we have the latest modification to the backend code. Note: The underlying JavaScript will not be updated by this process, so a refresh of the browser after clearing the output and saving is necessary to clear everything fully.
In [ ]:
import matplotlib
reload(matplotlib)
matplotlib.use('nbagg')
import matplotlib.backends.backend_nbagg
reload(matplotlib.backends.backend_nbagg)
In [ ]:
import matplotlib.backends.backend_webagg_core
reload(matplotlib.backends.backend_webagg_core)
import matplotlib.pyplot as plt
plt.interactive(False)
fig1 = plt.figure()
plt.plot(range(10))
plt.show()
In [ ]:
plt.plot([3, 2, 1])
plt.show()
In [ ]:
print(matplotlib.backends.backend_nbagg.connection_info())
In [ ]:
plt.close(fig1)
In [ ]:
plt.plot(range(10))
In [ ]:
print matplotlib.backends.backend_nbagg.connection_info()
In [ ]:
plt.show()
plt.figure()
plt.plot(range(5))
plt.show()
In [ ]:
plt.interactive(True)
plt.figure()
plt.plot([3, 2, 1])
Subsequent lines should be added to the existing figure, rather than creating a new one.
In [ ]:
plt.plot(range(3))
Disable interactive mode again.
In [ ]:
plt.interactive(False)
Unlike most of the other matplotlib backends, we may want to see a figure multiple times (with or without synchronisation between the views, though the former is not yet implemented). Assert that plt.gcf().canvas.manager.reshow() results in another figure window which is synchronised upon pan & zoom.
In [ ]:
plt.gcf().canvas.manager.reshow()
Saving the notebook (with CTRL+S or File->Save) should result in the saved notebook having static versions of the figues embedded within. The image should be the last update from user interaction and interactive plotting. (check by converting with ipython nbconvert <notebook>
)
In [ ]:
fig = plt.figure()
plt.axes()
plt.show()
plt.plot([1, 2, 3])
plt.show()
In [ ]:
from matplotlib.backends.backend_nbagg import new_figure_manager,show
manager = new_figure_manager(1000)
fig = manager.canvas.figure
ax = fig.add_subplot(1,1,1)
ax.plot([1,2,3])
fig.show()
In [ ]:
import matplotlib.animation as animation
import numpy as np
fig, ax = plt.subplots()
x = np.arange(0, 2*np.pi, 0.01) # x-array
line, = ax.plot(x, np.sin(x))
def animate(i):
line.set_ydata(np.sin(x+i/10.0)) # update the data
return line,
#Init only required for blitting to give a clean slate.
def init():
line.set_ydata(np.ma.array(x, mask=True))
return line,
ani = animation.FuncAnimation(fig, animate, np.arange(1, 200), init_func=init,
interval=32., blit=True)
plt.show()
After closing the previous figure (with the close button above the figure) the IPython keyboard shortcuts should still function.
The nbagg honours all colours appart from that of the figure.patch. The two plots below should produce a figure with a transparent background and a red background respectively (check the transparency by closing the figure, and dragging the resulting image over other content). There should be no yellow figure.
In [ ]:
import matplotlib
matplotlib.rcParams.update({'figure.facecolor': 'red',
'savefig.facecolor': 'yellow'})
plt.figure()
plt.plot([3, 2, 1])
with matplotlib.rc_context({'nbagg.transparent': False}):
plt.figure()
plt.plot([3, 2, 1])
plt.show()