##// END OF EJS Templates
support for unicode identifiers...
support for unicode identifiers This rewrites some of the regular expressions that are used to match Python identifiers, so that they are unicode compatible. In Python 3, identifiers can contain unicode characters as long as the first character is not numeric. Examples for the changes: • inputtransformer: ``` In [1]: π = 3.14 In [2]: π.is_integer? Object `is_integer` not found. ``` ---------- • namespace: ``` π.is_integ*? ``` or ``` In [1]: %psearch π.is_integ Python identifiers can only contain ascii characters. ``` ---------- • prefilter: ``` %autocall 1 φ = float get_ipython().prefilter("φ 3") # should be 'φ(3)', but returns 'φ 3' ``` ---------- • completerlib: If there is a file e.g. named `π.py` in the current directory, then ``` import IPython IPython.core.completerlib.module_list('.') # should contain module 'π' ```

File last commit:

r24425:3deabdbd
r25595:d9c0e690
Show More
Confined Output.ipynb
307 lines | 22.1 KiB | text/plain | TextLexer
/ tools / tests / Confined Output.ipynb

Test notebook for overflowing content

markdown image:

No description has been provided for this image

unconfined markdown image:

No description has been provided for this image
In [1]:
from IPython.display import Image, IFrame

Overflow image in HTML (non-embedded)

In [2]:
Image(url="http://placehold.it/800x200.png", embed=False)
Out[2]:
No description has been provided for this image

Overflow image:

In [3]:
Image(url="http://placehold.it/800x200.png", embed=True)
Out[3]:
No description has been provided for this image

Overflow, unconfined

In [4]:
Image(url="http://placehold.it/800x200.png", embed=True, unconfined=True)
Out[4]:
No description has been provided for this image

Overflow with explicit height, width (retina):

In [5]:
Image(url="http://placehold.it/1800x200.jpg", embed=True, retina=True)
Out[5]:
<IPython.core.display.Image object>

Overflowing IFrame:

In [ ]:
IFrame(src="https://ipython.org", width=900, height=400)
Out[ ]:

Overflowing table:

In [ ]:
import pandas as pd
pd.DataFrame([['column'] * 15])
Out[ ]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 column column column column column column column column column column column column column column column