Python's Future in Science Juan Nunez-Iglesias Victorian Life Sciences Computation Initiative (VLSCI) University of Melbourne at PyCon Australia 2016, Melbourne 

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http://bit.ly/prog-lang-in-astronomy 

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def simple_grep(pattern, file): for line in file: if pattern in line: yield line 

import numpy as np from scipy.stats import mstats def quantile_normalise(X): quantiles = np.mean(np.sort(X, axis=0), axis=1) ranks = mstats.rankdata(X, axis=0).astype(int) Xnorm = quantiles[ranks] return Xnorm 

flights %>% group_by(year, month, day) %>% select(arr_delay) %>% summarise(arrival.delay = median(arr_delay, na.rm = TRUE), num.flights = n()) %>% arrange(year, month, day) %>% mutate(date = date_from_tup(year, month, day), weekday = weekday_from_tup(year, month, day)) %>% ggplot(aes(y=arrival.delay, x=date, colour=weekday)) + geom_point() + scale_color_brewer(type="qual") 

−25 0 25 50 Jan 2013 Apr 2013 Jul 2013 Oct 2013 Jan 2014 date arrival.delay weekday Friday Monday Saturday Sunday Thursday Tuesday Wednesday 

fibonacci 0 = 0 fibonacci 1 = 1 fibonacci n = fibonacci (n - 1) + fibonacci (n - 2) 

fibonacci 0 = 0 fibonacci 1 = 1 fibonacci n = fibonacci (n - 1) + fibonacci (n - 2) fibonacci = 0 : 1 : zipWith (+) fibonacci (tail fibonacci) 

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https://benchmarksgame.alioth.debian.org 

a = 5 

int a = 5; a = 5 

struct _longobject { long ob_refcnt; PyTypeObject *ob_type; size_t ob_size; long ob_digit[1]; }; ... int a = 5; a = 5 

https://jakevdp.github.io/blog/2014/05/09/why-python-is-slow/ struct _longobject { long ob_refcnt; PyTypeObject *ob_type; size_t ob_size; long ob_digit[1]; }; ... int a = 5; a = 5 

def add(a, b): return a + b 

sum(my_list) np.sum(my_array) 

import numpy as np from scipy.stats import mstats def quantile_normalise(X): quantiles = np.mean(np.sort(X, axis=0), axis=1) ranks = mstats.rankdata(X, axis=0).astype(int) Xnorm = quantiles[ranks] return Xnorm 

Conn = (Adj + Adj.T) / 2 degs = np.ravel(Conn.sum(axis=0)) Degs = sparse.diags(1 / np.sqrt(degs)) Lap = Degs - Conn Q = Degs @ Lap @ Degs eigvals, eigvecs = eigsh(Q, k=3, which='SM') eigvecs = eigvecs[:, np.argsort(eigvals)] _, x, y = (Degs @ eigvecs).T 

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def f(x): y = x**4 - 3*x return y def integrate_f(a, b, n): dx = (b - a) / n dx2 = dx / 2 s = f(a) * dx2 for i in range(1, n): s += f(a + i * dx) * dx s += f(b) * dx2 return s http://bit.ly/aspp-cython 

def f(x): y = x**4 - 3*x return y def integrate_f(a, b, n): dx = (b - a) / n dx2 = dx / 2 s = f(a) * dx2 for i in range(1, n): s += f(a + i * dx) * dx s += f(b) * dx2 return s cdef double f(double x): cdef double y = x**4 - 3*x return y def integrate_f(double a, double b, int n): cdef: double dx = (b - a) / n double dx2 = dx / 2 double s = f(a) * dx2 int i = 0 for i in range(1, n): s += f(a + i * dx) * dx s += f(b) * dx2 return s http://bit.ly/aspp-cython 

https://jakevdp.github.io/blog/2014/05/09/why-python-is-slow/ struct _longobject { long ob_refcnt; PyTypeObject *ob_type; size_t ob_size; long ob_digit[1]; }; 

function quadratic(a, sqr_term, b) return (-b + sqr_term) / 2a end function quadratic2(a, b, c) sqr_term = sqrt(b^2-4a*c) r1 = quadratic(a, sqr_term, b) r2 = quadratic(a, -sqr_term, b) return r1, r2 end adapted from https://samuelcolvin.github.io/JuliaByExample 

def euclidean_distance(a, b): n = len(a) sqdist = 0 for i in range(n): d = a[i] - b[i] sqdist += d * d return np.sqrt(sqdist) 

def f(x): y = x**4 - 3*x return y def integrate_f(a, b, n): dx = (b - a) / n dx2 = dx / 2 s = f(a) * dx2 for i in range(1, n): s += f(a + i * dx) * dx s += f(b) * dx2 return s 

def f(x): y = x**4 - 3*x return y def integrate_f(a, b, n): dx = (b - a) / n dx2 = dx / 2 s = f(a) * dx2 for i in range(1, n): s += f(a + i * dx) * dx s += f(b) * dx2 return s import numba @numba.jit(nopython=True) def f(x): y = x**4 - 3*x return y @numba.jit(nopython=True) def integrate_f(a, b, n): dx = (b - a) / n dx2 = dx / 2 s = f(a) * dx2 for i in range(1, n): s += f(a + i * dx) * dx s += f(b) * dx2 return s 

def euclidean_distance(a, b): n = len(a) sqdist = 0 for i in range(n): d = a[i] - b[i] sqdist += d * d return np.sqrt(sqdist) 

def euclidean_distance(a, b): n = len(a) sqdist = 0 for i in range(n): d = a[i] - b[i] sqdist += d * d return np.sqrt(sqdist) https://jakevdp.github.io/blog/2012/09/20/why-python-is-the-last/ 

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Take-homes Community Elegance Performance 

Take-homes Community Elegance Performance BONUS: USE PYTHON 3.5! 

Questions? [email protected] @jnuneziglesias (Twitter) @jni (GitHub)