

In [1]:

    
!cat /biipy/version









    



v1.6.0

Check numpy



In [2]:

    
import numpy as np
a = np.arange(100)
a









    Out[2]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])



In [3]:

    
np.__config__.show()









    



lapack_opt_info:
    define_macros = [('HAVE_CBLAS', None)]
    libraries = ['openblas']
    language = c
    library_dirs = ['/usr/lib']
blas_opt_info:
    define_macros = [('HAVE_CBLAS', None)]
    libraries = ['openblas']
    language = c
    library_dirs = ['/usr/lib']
blas_mkl_info:
  NOT AVAILABLE
openblas_info:
    define_macros = [('HAVE_CBLAS', None)]
    libraries = ['openblas']
    language = c
    library_dirs = ['/usr/lib']
openblas_lapack_info:
    define_macros = [('HAVE_CBLAS', None)]
    libraries = ['openblas']
    language = c
    library_dirs = ['/usr/lib']

Check matplotlib



In [4]:

    
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot(a);

Check numba



In [5]:

    
def f(x):
    y = np.empty_like(x)
    for i in range(x.size):
        y[i] = x[i] * 2
    return y



In [6]:

    
x = np.arange(1000000)



In [7]:

    
%timeit f(x)









    



1 loops, best of 3: 297 ms per loop



In [8]:

    
import numba



In [9]:

    
fjit = numba.jit(f)



In [10]:

    
%timeit fjit(x)









    



The slowest run took 57.15 times longer than the fastest. This could mean that an intermediate result is being cached 
1000 loops, best of 3: 1.43 ms per loop



In [11]:

    
np.array_equal(f(x), fjit(x))









    Out[11]:





True

Check bokeh



In [12]:

    
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
output_notebook()









    






    
    
        
        BokehJS successfully loaded.



In [13]:

    
n = 4000
x = np.random.random(size=n) * 100
y = np.random.random(size=n) * 100
radii = np.random.random(size=n) * 1.5
colors = ["#%02x%02x%02x" % (int(r), int(g), 150) for r, g in zip(np.floor(50+2*x), np.floor(30+2*y))]



In [14]:

    
p = figure()
p.circle(x, y, radius=radii, fill_color=colors, fill_alpha=.9, line_color=None)









    Out[14]:





<bokeh.models.renderers.GlyphRenderer at 0x7f1b29431358>



In [15]:

    
show(p)









    






    







    Out[15]:





<bokeh.io._CommsHandle at 0x7f1afd2ca0b8>



In [ ]: