In-notebook NumPy man pages
Project description
# npdoc.py
I do a lot of work entirely in an IPython notebook and it can be annoying to switch back and forth from a browser to the command line to look up function usage. I therefore wrote this little utility. Given a numpy function name, it will look up the function's source on GitHub, and parse the usage comment at the top of the function.
This code requires the BeautifulSoup 4 library, which is readily pip'd.
Here's an example:
```
>> import npdoc
>> npd('meshgrid')
>> Return coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of
N-D scalar/vector fields over N-D grids, given
one-dimensional coordinate arrays x1, x2,..., xn.
.. versionchanged:: 1.9
1-D and 0-D cases are allowed.
...
```
(Not showing the full output because there's a lot)
You can also specify to only see the first or last lines with the ```nl``` argument. A positive argument gives the first N lines:
```
>> import npdoc
>> npd('meshgrid', nl = 4)
>> Return coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of
N-D scalar/vector fields over N-D grids, given
```
And a negative argument gives the last N lines:
```
>> import npdoc
>> npd('meshgrid', nl = -4)
>> >>> y = np.arange(-5, 5, 0.1)
>>> xx, yy = np.meshgrid(x, y, sparse=True)
>>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
>>> h = plt.contourf(x,y,z)
```
Note: in the case of the base NumPy functions, like ```np.array```, the source is more difficult to get to and to parse (those functions are written in C, too), so in that case, ```npd()``` will just open a browser with the formatted webpage. If you just want to go straight to the formatted webpage anyway, just do ```npd('meshgrid', browser = True)```.
I do a lot of work entirely in an IPython notebook and it can be annoying to switch back and forth from a browser to the command line to look up function usage. I therefore wrote this little utility. Given a numpy function name, it will look up the function's source on GitHub, and parse the usage comment at the top of the function.
This code requires the BeautifulSoup 4 library, which is readily pip'd.
Here's an example:
```
>> import npdoc
>> npd('meshgrid')
>> Return coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of
N-D scalar/vector fields over N-D grids, given
one-dimensional coordinate arrays x1, x2,..., xn.
.. versionchanged:: 1.9
1-D and 0-D cases are allowed.
...
```
(Not showing the full output because there's a lot)
You can also specify to only see the first or last lines with the ```nl``` argument. A positive argument gives the first N lines:
```
>> import npdoc
>> npd('meshgrid', nl = 4)
>> Return coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of
N-D scalar/vector fields over N-D grids, given
```
And a negative argument gives the last N lines:
```
>> import npdoc
>> npd('meshgrid', nl = -4)
>> >>> y = np.arange(-5, 5, 0.1)
>>> xx, yy = np.meshgrid(x, y, sparse=True)
>>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)
>>> h = plt.contourf(x,y,z)
```
Note: in the case of the base NumPy functions, like ```np.array```, the source is more difficult to get to and to parse (those functions are written in C, too), so in that case, ```npd()``` will just open a browser with the formatted webpage. If you just want to go straight to the formatted webpage anyway, just do ```npd('meshgrid', browser = True)```.
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