Posts about ipython (old posts, page 2)

2016-06-25 compiling notebooks into a report

For a master's project in computational neuroscience, we adopted a quite novel workflow to go all the steps from the learning of the small steps to the wrtiting of the final thesis. Though we were flexible in our method during the 6 months of this work, a simple workflow emerged that I describe here.

Compiling a set of notebook to a LaTeX document.

Read more…

Comments

2016-06-01 Compiling and using pyNN + NEST + python3

PyNN is a neural simulation language which works well with the NEST simulator. Here I show my progress in using both with python 3 and how to show results in a notebook.

Read more…

Comments

2016-02-19 Compiling and using pyNN + NEST + python3

PyNN is a neural simulation language which works well with the NEST simulator. Here I show my progress in using both with python 3 and how to show results in a notebook.

Read more…

Comments

2015-02-25 Saving files in HoloViews

Here, we show how to save files from figures generated with HoloViews. This was thoroughly explained in this page.

Read more…

Comments

2015-02-17 Creating an animation using Gizeh + MoviePy

Gizeh (that is, Cairo for tourists) is a great interface to the Cairo drawing library.

I recently wished to make a small animation of a bar moving in the visual field and crossing a simple receptive field to illustrate some simple motions that could be captured in the primary visual cortex ansd experiments that could be done there.

Read more…

Comments

2015-01-20 Using Tikzmagic

TIKZ is a great language for producing vector graphics. It is however a bit tedious to go over the whole $\LaTeX$-like compilation when you get used to an ipython notebooks work-flow.

I describe here how to use a cell magic implemented by http://www2.ipp.mpg.de/~mkraus/python/tikzmagic.py and a hack to use euclide within the graph (as implemented in https://github.com/laurentperrinet/ipython_magics).

Read more…

Comments

2015-01-16 Rendering 3D scenes in python

The above snippet shows how you can create a 3D rendered scene in a few lines of codes (from http://zulko.github.io/blog/2014/11/13/things-you-can-do-with-python-and-pov-ray/):

In [1]:
import vapory

camera = vapory.Camera( 'location', [0, 2, -3], 'look_at', [0, 1, 2] )
light = vapory.LightSource( [2, 4, -3], 'color', [1, 1, 1] )
sphere = vapory.Sphere( [0, 1, 2], 2, vapory.Texture( vapory.Pigment( 'color', [1, 0, 1] )))

scene = vapory.Scene(camera = camera , # a Camera object
                     objects = [light, sphere], # POV-Ray objects (items, lights)
                     included = ["colors.inc"]) # headers that POV-Ray may need

# passing 'ipython' as argument at the end of an IPython Notebook cell
# will display the picture in the IPython notebook.
scene.render('ipython', width=900, height=500)
Out[1]:

Here are more details...

Read more…

Comments

2015-01-07 The right imports in a notebook

Following this post http://carreau.github.io/posts/10-No-PyLab-Thanks.ipynb.html, here is ---all in one single cell--- the bits necessary to import most useful libraries in an ipython notebook:

In [1]:
# import numpy and set the printed precision to something humans can read
import numpy as np
np.set_printoptions(precision=2, suppress=True)
# set some prefs for matplotlib
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams.update({'text.usetex': True})
fig_width_pt = 700.  # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27               # Convert pt to inches
fig_width = fig_width_pt*inches_per_pt  # width in inches
FORMATS = ['pdf', 'eps']
phi = .5*np.sqrt(5) + .5 # useful ratio for figures
# define plots to be inserted interactively
%matplotlib inline
#%config InlineBackend.figure_format='retina' # high-def PNGs, quite bad when using file versioning
%config InlineBackend.figure_format='svg'

Below, I detail some thoughts on why it is a perfect preamble for most ipython notebooks.

Read more…

Comments

2014-12-09 Polar bar plots

I needed to show prior information for the orientation of contours in natural images showing a preference for cardinal axis. A polar plot showing seemed to be a natural choice for showing the probability distribution function. However, this seems visually flawed...

Read more…

Comments