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:
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# 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.
import numpy¶
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)
The golden section ($\phi \approx 1.618033$) gives a nice ratio between the width and height of a figure, it is also close to your display (imacs and many recent displays have a ratio of 16/10) or your phone / tablet / ...
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phi = .5*np.sqrt(5) + .5
print r'\phi = ', phi
$\LaTeX$¶
If you aim at pinting nicely formatted fgures to include into some $\LaTeX$ code, you maywish to include:
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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
if you export your files to different formats, you may create a list for that:
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FORMATS = ['pdf', 'eps']
import interactive pylab¶
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%matplotlib inline
%config InlineBackend.figure_format='retina'
import matplotlib.pyplot as plt
More options can be obtained using ipython's functions:
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from IPython.core.pylabtools import figsize, getfigs
such as :
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figsize(fig_width, fig_width/phi)
sample¶
install the LogGabor package
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!pip install SLIP
!pip install LogGabor
!pip install NeuroTools
retrive lena
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!curl http://upload.wikimedia.org/wikipedia/en/2/24/Lenna.png -o /tmp/lena.png
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from SLIP import Image
from LogGabor import LogGabor
from NeuroTools.parameters import ParameterSet
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image = plt.imread('/tmp/lena.png').mean(axis=-1)
## alternatively, you may use :
# from scipy.misc import lena
# image = lena()
opts= {'vmin':0., 'vmax':1., 'interpolation':'nearest', 'origin':'upper'}
N_X, N_Y = image.shape
#pe = ParameterSet('default_param.py')
im = Image(ParameterSet({'N_X':N_X, 'N_Y': N_Y}))
lg = LogGabor(im)
fig = plt.figure(figsize=(fig_width, fig_width/phi))
xmin, ymin, size = 0, 0, 1.
for i_level in range(8):
a = fig.add_axes((xmin/phi, ymin, size/phi, size), axisbg='w')
a.axis(c='b', lw=0)
plt.setp(a, xticks=[])
plt.setp(a, yticks=[])
im_RGB = np.zeros((N_X, N_Y, 3))
for theta in np.linspace(0, np.pi, 8, endpoint=False):
params = {'sf_0':1./(2**i_level), 'B_sf':.4, 'theta':theta, 'B_theta':3.14159/18.}
# loggabor takes as args: u, v, sf_0, B_sf, theta, B_theta)
FT_lg = lg.loggabor(0, 0, **params)
im_abs = np.absolute(im.FTfilter(image, FT_lg, full=True))
RGB = np.array([.5*np.sin(2*theta + 2*i*np.pi/3)+.5 for i in range(3)])
im_RGB += im_abs[:,:, np.newaxis] * RGB[np.newaxis, np.newaxis, :]
im_RGB /= im_RGB.max()
a.imshow(1. - im_RGB, **opts)
a.grid(False)
i_orientation = np.mod(i_level, 4)
if i_orientation==0:
xmin += size
ymin += size/phi**2
elif i_orientation==1:
xmin += size/phi**2
ymin += -size/phi
elif i_orientation==2:
xmin += -size/phi
elif i_orientation==3:
ymin += size
size /= phi
#print i_orientation, xmin, ymin, size
for ext in FORMATS: fig.savefig('/tmp/fig_log_gabor_filters.' + ext, dpi=450)
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getfigs()
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