upstream/ipython Files · examples/tests/pylab_figshow.py

Merge pull request from minrk/task-history...

Merge pull request from minrk/task-history change default behavior of database task storage The previous default behavior was to create a new table (sqlite) / database (mongodb) for each Hub instance, so that task history would not be shared by subsequent IPython sessions. I don't think this was the right choice, so this PR switches the default behavior to use the table / database ipython-tasks. The result is that all Hub sessions in a given profile will share history by default, which makes the benefit of the db backends clearer. Configurable behavior remains the same, only the default is changed.

Brian Granger - - Load All Authors

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      """Manual test for figure.show() in the inline matplotlib backend.

      This script should be loaded for interactive use (via %load) into a qtconsole

      or notebook initialized with the pylab inline backend.  

      Expected behavior: only *one* copy of the figure is shown.

      For further details:

      https://github.com/ipython/ipython/issues/1612

      https://github.com/matplotlib/matplotlib/issues/835

      """

      import numpy as np

      import matplotlib.pyplot as plt

      plt.ioff()                                                                      

      x = np.random.uniform(-5, 5, size=(100))

      y = np.random.uniform(-5, 5, size=(100))

      f = plt.figure()

      plt.scatter(x, y)

      plt.plot(y)

      f.show()

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				"""Manual test for figure.show() in the inline matplotlib backend.

				This script should be loaded for interactive use (via %load) into a qtconsole
				or notebook initialized with the pylab inline backend.

				Expected behavior: only one copy of the figure is shown.


				For further details:
				https://github.com/ipython/ipython/issues/1612
				https://github.com/matplotlib/matplotlib/issues/835
				"""

				import numpy as np
				import matplotlib.pyplot as plt

				plt.ioff()
				x = np.random.uniform(-5, 5, size=(100))
				y = np.random.uniform(-5, 5, size=(100))
				f = plt.figure()
				plt.scatter(x, y)
				plt.plot(y)
				f.show()