

In [3]:

    
import numpy as np
import vaex as vx
import pylab
ds = vx.open("/home/data/gavi/Aq-A-2-999-shuffled-10percent.hdf5")
print ds
#server = vx.server("localhost")
#ds = server.open("../data/Aq-A-2-999-shuffled-1percent.hdf5")
%matplotlib inline
table









    



<vaex.dataset.Hdf5MemoryMapped object at 0x2b16b32a7890>






    Out[3]:





<vaex.dataset.Hdf5MemoryMapped at 0x2b156c198cd0>

Simplest example

The default for plot is to calculate the mean +/- 3 sigma for the limits, and plot the density plot



In [4]:

    
subspace = ds("x", "y")
subspace.plot(f=np.log)

More control

For more control, you can find the limits yourself, with minmax or limits_sigma, do the histogram, and let vaex handle the plotting.

Vaex uses matplotlib, so any matplotlib routines before and afterwards are fine.



In [5]:

    
limits = subspace.limits_sigma(square=True, sigmas=0.4)
means = subspace.mean()
print "limits", limits









    



limits [[ 56.19850387  57.53320609]
 [ 51.74976341  53.08446563]]



In [6]:

    
grid = subspace.histogram(size=256, limits=limits)



In [7]:

    
pylab.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
subspace.plot(np.log10(grid+1), limits, center=means)
pylab.scatter(0, 0)









    Out[7]:





<matplotlib.collections.PathCollection at 0x2b16b339b510>

More control

Extra keywords will be passed to pylab.imshow, such as cmap for the colormap



In [8]:

    
limits = subspace.limits_sigma(sigmas=4, square=True)
grid = subspace.histogram(size=512, limits=limits)
pylab.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
subspace.plot(grid, limits=limits, f=np.log1p, cmap='afmhot')



In [9]:

    
#reload_ext autoreload
%load_ext autoreload
%autoreload 2

Selections

Selections can be done using expressions as well. To use the selection, use subspace.selected()



In [10]:

    
ds.select("(z<48)")
selected = subspace.selected()
grid_masked1 = selected.histogram(size=512, limits=limits)
ds.select("(z>50)")
grid_masked2 = selected.histogram(size=512, limits=limits)



In [11]:

    
cmap = 'afmhot'
pylab.figure(num=None, figsize=(10, 10), dpi=80, facecolor='w', edgecolor='k')
subspace.plot(np.log(grid+1), limits=limits, cmap=cmap, alpha=1.0)
#subspace.plot(np.log(grid_masked), limits=limits, cmap=cmap, alpha=0.5)
pylab.contour(np.log(grid_masked1), 2, extent=np.array(limits).flatten(), alpha=0.8, colors="green")
pylab.contour(np.log(grid_masked2), 2, linewidth="2pt", colors="blue", extent=np.array(limits).flatten(), alpha=0.8)









    Out[11]:





<matplotlib.contour.QuadContourSet instance at 0x2b17c421fb48>



In [12]:

    
subspace = ds("x", "y")



In [13]:

    
subspace.mean()









    Out[13]:





array([ 56.86554467,  52.41758988])



In [2]:

    
ls









    



example_1.ipynb       Untitled1.ipynb  Untitled5.ipynb  volume rendering.ipynb
example_server.ipynb  Untitled2.ipynb  Untitled6.ipynb
gums_mass.ipynb       Untitled3.ipynb  Untitled.ipynb
tutorial.ipynb        Untitled4.ipynb  vaexclient.py



In [ ]: