anaconda.ipynb

Kernel: Python 3 (Anaconda)

import sys
print(sys.executable)
sys.version

/ext/anaconda3/bin/python3

'3.5.4 | packaged by conda-forge | (default, Dec  9 2017, 16:18:50) \n[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)]'

import cvxpy
cvxpy.__version__

'0.4.8'

import pandas_datareader as pdr
from pandas_datareader import data, wb
pdr.__version__

'0.6.0'

import geopandas
geopandas.__version__

'0.3.0'

import cartopy
cartopy.__version__

'0.16.0'

import rasterstats as ra
ra.__version__

'0.12.1'

import scipy
scipy.__version__

'1.0.0'

import sklearn
sklearn.__version__

'0.19.1'

import pyximport; pyximport.install()
import scipy_special
scipy_special.f(1.2, 3.2)

0.9207537886523123

def f(x):
    return 2*x +1
def g(a, n):
    b = f(a)
    for i in range(n):
        b = f(a) + f(b)
    return b

%load_ext line_profiler

The line_profiler extension is already loaded. To reload it, use:
  %reload_ext line_profiler

%lprun -f g g(5, 100)

Timer unit: 1e-06 s

Total time: 0.000585 s
File: <ipython-input-41-e4e240bf24fd>
Function: g at line 3

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     3                                           def g(a, n):
     4         1          7.0      7.0      1.2      b = f(a)
     5       101        102.0      1.0     17.4      for i in range(n):
     6       100        475.0      4.8     81.2          b = f(a) + f(b)
     7         1          1.0      1.0      0.2      return b

%lprun -f f g(5, 100)

Timer unit: 1e-06 s

Total time: 7.8e-05 s
File: <ipython-input-41-e4e240bf24fd>
Function: f at line 1

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     1                                           def f(x):
     2       201         78.0      0.4    100.0      return 2*x +1

!hostname

project-20e4a191-73ea-4921-80e9-0a5d792fc511

4+4

8

2+2+5

9

import sys; sys.executable

'/ext/anaconda3/bin/python3'

$\int_0^\infty x\; \mathrm{d}x$

import numpy as np; np; np.__version__

'1.12.1'

import pandas as pd

import numba
print(numba)
numba.__version__

<module 'numba' from '/ext/anaconda3/lib/python3.5/site-packages/numba/__init__.py'>

'0.36.2'

def faster(x, y):
    a = 0.0
    for i in range(x):
        for j in range(i, x):
            b = a
            a += y * .2
            a -= y * .1
            a = (a + b) / 2.
    return a

%timeit faster(2222, 1.111)

500 ms ± 97.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

import numba
faster2 = numba.autojit(faster)

%timeit faster2(2222, 1.111)

11.3 ms ± 501 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Test

def f2(x, y):
    k = 0
    for i in range(x):
        for j in range(y):
            k += i / x + j / y
    return k

%timeit f2(200, 300)

8.09 ms ± 1.28 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)

@numba.jit(nopython=True, nogil=True)
def f(x, y):
    k = 0
    for i in range(x):
        for j in range(y):
            k += i / x + j / y
    return k

%timeit f(200, 300)

107 µs ± 2.97 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

%matplotlib inline
import matplotlib.pyplot as plt

plt.plot([f(x, 2.2) for x in range(1000)])

[<matplotlib.lines.Line2D at 0x7f8421158208>]

np.__version__

'1.12.1'