Csabai István, ELTE Komplex Rendszerek Fizikája Tanszék
Based on: (http://nbviewer.ipython.org/gist/rpmuller/5920182)
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
A Python egy dinamikusan fejlődő kutatók és nem kutatók körében egyre népszerűbb programozási nyelv. Népszerűsége többek közt annak köszönhető, hogy nagyon összetett feladatokat is könnyen meg lehet vele oldani, és viszonylag gyorsan bele lehet tanulni, egyszerűen telepíthető, számos nagyon kényelmes keretrendszer áll rendelkezésre. Szokás mondani, hogy ez egy olyan nyelv, amit pár nap alatt megtanulhatsz, és egész életed során használhatod.
A Python Tutorial (angolul) egy kiváló oldal, ahol általában a Python tanulását el lehet kezdeni, rengeteg példa található ott. Ez a jegyzet a nemrég megjelent IPython Project keretrendszerét használja fel, amely lehetővé teszi egy Notebook, azaz jegyzőkönyv formájában a szövegesen, képletekkel vagy ábrák segítségével adott információk és a Python kód integrációját.
További hasznos linkek (angolul):
A gyakorlaton leginkább egy felhő (cloud) szolgáltatást a SageMathCloud-ot használjuk. Ennek hatalmas előnye, hogy semmit nem kell gépünkre telepíteni, regisztráció után, operációs rendszertől függetlenül, web-böngészőből használható. Ráadásul nem csak a Python programozást lehet rajta keresztül kipróbálni, hanem a Sage szimbolikus matematikai keretrendszert, a LaTeX dokument-szerkesztőt vagy akár egy virtuális terminálon a Linux rendszert is.
Álljon azért itt röviden (egyelőre angolul) egy tájékoztató, hogy hogyan lehet saját gépre telepíteni a rendszert:
There are two branches of current releases in Python: the older-syntax Python 2, and the newer-syntax Python 3. This schizophrenia is largely intentional: when it became clear that some non-backwards-compatible changes to the language were necessary, the Python dev-team decided to go through a five-year (or so) transition, during which the new language features would be introduced and the old language was still actively maintained, to make such a transition as easy as possible. We're now (2013) past the halfway point, and, IMHO, at the first time when I'm considering making the change to Python 3.
Nonetheless, I'm going to write these notes with Python 2 in mind, since this is the version of the language that I use in my day-to-day job, and am most comfortable with. If these notes are important and are valuable to people, I'll be happy to rewrite the notes using Python 3.
With this in mind, these notes assume you have a Python distribution that includes:
A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the Entought Python Distribution, also known as EPD, which appears to be changing its name to Enthought Canopy. Enthought is a commercial company that supports a lot of very good work in scientific Python development and application. You can either purchase a license to use EPD, or there is also a free version that you can download and install.
Here are some other alternatives, should you not want to use EPD:
Linux Most distributions have an installation manager. Redhat has yum, Ubuntu has apt-get. To my knowledge, all of these packages should be available through those installers.
Mac I use Macports, which has up-to-date versions of all of these packages.
Windows The PythonXY package has everything you need: install the package, then go to Start > PythonXY > Command Prompts > IPython notebook server.
Cloud This notebook is currently not running on the IPython notebook viewer, but will be shortly, which will allow the notebook to be viewed but not interactively. I'm keeping an eye on Wakari, from Continuum Analytics, which is a cloud-based IPython notebook. Wakari appears to support free accounts as well. Continuum is a company started by some of the core Enthought Numpy/Scipy people focusing on big data.
Continuum also supports a bundled, multiplatform Python package called Anaconda that I'll also keep an eye on.
IPython tutorial in your copious free time.
Briefly, notebooks have code cells (that are generally followed by result cells) and text cells. The text cells are the stuff that you're reading now. The code cells start with "In []:" with some number generally in the brackets. If you put your cursor in the code cell and hit Shift-Enter, the code will run in the Python interpreter and the result will print out in the output cell. You can then change things around and see whether you understand what's going on. If you need to know more, see the IPython notebook documentation or the IPython tutorial.
A Notebook-ok cellákból állnak. Az In[]
cellákba gépelhetünk szöveget (menü:"Markdown"), vagy utasításokat (menü:"Code"), míg az Out[]
cellákban jelenik meg a kimenet. Az utasítások végrehajtását a meüben lévő lejátszás gombbal, vagy a Shift+ENTER
(utána ugrás a következő cellára) vagy a Ctrl+ENTER
(marad a cellában) leütésével indíthatjuk. A régebbi cellák átírhatóak, újra futtathatóak. A kimenet nem csak számokból állhat, hanem grafikonok és ábrák is lehetnek.
A Python úgy nevezett interpreteres nyelv, azaz az utasítások a több más nyelvre (pl. C, C++, Fortran, Java) jellemző továbbí fordítási lépés nélkül azonnal értelmezésre kerül, Ennek megfelelően, legegyszerűbb esetben használhatjuk számológépnek is:
2+2
(50-5*6)/4
Figyelni kell azonban, mert a Python máshogy kezeli az egész számokat (integer
v. int
) és a törteket reprezentáló úgynevezett lebegőpontos (float
v. double
) számokat:
7/3
A C és Fortran nyelvekhet hasonlóan az egész-osztást az egész számokon végrehajtva kerekített egész számot kapunk végeredményként. Ha azt szeretnénk, hogy a Python valós (azaz lebegőpontos) számnak tekintse a beadott értéket, akkor azt a tizedes pont (!! Figyelem: itt is, mint ahogy szinte minden számítógépes környezetben, az angolszász jelölést használják, azaz nem tizedes vesszőt, hanem tizedes pontot!!) kitételével jelezhetjük.
7/3.
Másik lehetőség a szám típusának explicit, azaz direkt megadása:
7/float(3)
A Python nyelv alapkészletében csak nagyon egyszerű műveletek állnak rendelkezésre, így például már olyan egyszerű műveleteket, mint a gyökvonás se tartalmazza. Ha nem ismert műveletet írunk be, hibaüzenetet kapunk.
sqrt(81)
Számtalan kiegészítő program-könyvtár, azaz library hívható be az import
parancs segítségével. Pl. a math
könyvtár sqrt
függvénye így hívható be:
from math import sqrt
sqrt(81)
Alternatív megoldás, hogy nem csak egy függvényt, hanem a teljes könyvtárat beolvastatjuk:
import math
math.sqrt(81)
Mint ahogy a matematikai egyenletekben az ismeretleneket, a programnyelvekben is elnevezhetjük az úgy nevezett változókat. Az egyenlőség (=) jel segítségével adhatunk nekik értéket:
x=3
A változóknak adhatunk hosszabb, több betűből álló nevet is. Kerüljük az ékezetes és speciális karakterek használatát!
width = 20
length = 30
area = length*width
area
Ha olyan változó értékét szeretnénk használni, amely még nem lett definiálva, hibát kapunk:
volume
Más származtatott változókat is használhatunk az új változók defininíciójához:
depth = 10
volume = area*depth
volume
Szinte bármilyen változónevet adhatunk, de vannak szabályok. Ne használjunk ékezetes karaktereket! A változónak betűvel vagy "_" jellel kell kezdődnie. A ("_")-n kívül más speciális karaktert ne használjunk!. Bizonyos szavak már uatsítások céljára foglaltak:
and, as, assert, break, class, continue, def, del, elif, else, except,
exec, finally, for, from, global, if, import, in, is, lambda, not, or,
pass, print, raise, return, try, while, with, yield
Ha ilyeneket akarunk definiálni, szintaktikai hibát kapunk:
return = 0
A kis és NAGY betűk, illetve KoMbináciÓik külön kezelődnek:
Alma=11
alma=27
AlMa=9
ALma=3
alMA=15
Alma
alma
ALma+alMA
A String-ek karakterek (betűk számok, írásjelek, illetve egyéb egzotikus jelek mint pl @ & %) egymás után fűzött láncai, melyeket szimpla idézőjelekkel
'Hello, World!'
vagy dupla idézőjelekkel definiálhatunk
"Hello, World!"
Mivel mind a két fajta idézőjel is karakter ezért ha ügyelnünk kell olyan stringek definiálásánál amely valamelyiket tartalmazhatja.
"He's a Rebel"
'She asked, "How are you today?"'
Mint ahogy a már megismert adatípusoknál is megszokhattuk (int-ek és float-ok) egy változónak adhatunk string títusú értéket.
greeting = "Hello, World!"
A print parancs segítségével megjeleníthetünk string-eket:
print greeting
Ezen kívül más típusú változók megjelenítésére is használhatjuk:
print "The area is ",area
A fenti példában az area nevű változó értéke 600, melyet a print parancs automatikusan string tipúsra konvertál megjelenítés előtt.
A "+" operátor segítségével két string típusú változót egymás után fűzhetünk:
statement = "Hello," + "World!"
print statement
Figyelem a szóköz is karakter, ha a kiírandó stringeket nem szorosan egymás akarjuk kiírni hanem szóközzel akarjuk elválasztani őket akkor a megelelő helyre szóközöket kell tenni!
statement = "Hello," + "World!"
print statement
statement = "Hello, " + "World!"
print statement
statement = "Hello,"+ " " + "World!"
print statement
A fenti példák közzül az utolsóban 1 sorban 3 stringet fűztünk össze egymás után. Hasonló módon több string-et is egymás után tudunk fűzni:
print "This " + "is " + "a " + "longer " + "statement."
Ha sok stringet akarunk össze fűzni akkor arra vannak elegánsabb megoldások is de a fenti megoldás néhány string össze fűzésére elegendő.
Programozás közben sokszor előfordul hogy hasonló típusú objektumokat, szeretnénk együtt kezelni. A python ezt a problémát a list típusú változók segítségével kezeli. Például szeretnénk a hét napjait (mondjuk egy naptár alkalmazás keretein belül) együtt kezelni, Ekkor a [,] szögletes zárójelek segítségével definálhatjuk a következő változót:
days_of_the_week = ["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"]
azaz a days_of_the_week egy olyan list melynek string típusú elemei vannak és minden elem megfelel egy napnak.
A megfelelő elemhez való hozzáférést az elem index-én keresztül történik, szintén a [,] szögletes zárójelek segítségével:
days_of_the_week[2]
A python list-ek, első eleme a 0-as index-hez tartozik. (Ez hasonló a C nyelvbeni konvencióhoz de például ellenkezik a Fortran nyelv konvenciójával). Tehát a fenti páldában a 0-as elem a "Sunday", az 1-es a "Monday" és így tovább. Pythonban egy lista végén szereplő elemeit negatív index-ekkel érhetjük el. Például a -1-es elem a fenti listában:
days_of_the_week[-1]
Egy listához az ** .append() ** parancs segítségével fűzhetünk további elemeket:
languages = ["Fortran","C","C++"]
languages.append("Python")
print languages
A range() parancs segítségével számok egyszerű sorozatait hozhatjuk létre:
range(10)
Figyeljük meg hogy a fenti lista 0-val kezdődik és csak 9-ig tart, azaz 0-tól a 10-nél kisebb számok sorozata. Ha a range parancsot két paraméterrel hívjuk meg range(start,stop) akkor a lista elemei nem 0-val hanem a start értékkel kezdődnek:
range(2,8)
Egy harmadik paraméter segítségével az egymást követő tagok lépés különbségét változtathatjuk. range(start,stop,step) Az alábbi példában a harmadik paraméter miatt a listában 0-tól 20-ig csak minden második elem jelenik meg:
evens = range(0,20,2)
evens
evens[3]
Egy list-be nem csak ugyan olyan típusú objektumokat tehetünk. Például:
["Today",7,99.3,""]
Enek ellenére azért mindíg célszerű olyan objektumokat egy listába foglalni amik valamilyen logikus kapcsolatban állnak egymással. Programozás technikailag célszerűbb különböző típusú objektumokat nem list-ekbe hanem inkább tuple-k ba foglani, ezekre a későbbiekben fogunk látni konkrét példát.
Egy lista hosszát a len() parancs adja meg:
help(len)
len(evens)
A számítógépek legfontosabb tulajdonságai közt szerepel az, hogy nagyon gyorsak és "fáradhatatlanok". Olyan feladatok megoldásában a leghatékonyabbak, amikor a feladatot kevés munkával meg lehet fogalmazni ("az alkotó pihen") de végrehajtása nagyon sok ismétlést, iterációt igényel ("a gép forog"). Az iteráció (angol: iterate) azt jelenti, hogy például egy lista elemein egyesével végigmegy a program, és műveleteket végez el rajtuk. A Python-ban, az egyik erre használható utasítás a for parancs (magyarul kb. a ...-ra, azaz pl. a hét minden napjára, a lista minden elemére):
for day in days_of_the_week:
print day
Ez a kódrészlet a days_of_the_week listán megy végig és a meglátogatott elemet hozzárendeli a day változóhoz, amit ciklusváltozónak is neveznek. Ezekután mindent végrehajt amit a beljebb tabulált (angolul: indented) parancsblokkban írtunk (most csak egy print utasítás), amihez felhasználhatja a ciklusváltozót is. Miután vége a beljebb tabulált régiónak, kilép a ciklusból.
Szinte minden programnyelv használ hasonló ciklusokat. A C, C++, Java nyelvekben kapcsos zárójeleket {} használnak a ciklusok elkülönítésére, kb. így:
for (i=1..10) {print i}
A FORTRAN nyelvben az END szócska kiírása jelzi a ciklus végét. A pythonban a kerrőspont (":"), majd vagy tabulátor-ral, vagy szóközökkel beljebb írt sorok szolgálnak erre. FIGYELEM: a kettőt egy programban ne keverjük, vagy TAB vagy szóközök! Mivel a szövegszerkesztőben a képernyőn pár szóköz ugyanúgy néz ki mint egy TAB, ez a megoldás gyakran megtréfálhatja a kezdő programozót. Különösen gond lehet, ha más programokból másolunk át részleteket.
Annak semmi jelentősége nincs, hogy a példában a day nevet adtunk az iterációban szereplő ciklusváltozónak. A program semmit se tud az emberi időszámításról, például, hogy a hétben napok vannak és nem kiscicák:
for macska in days_of_the_week:
print macska
A ciklus utasításblokkja állhat több utasításból is:
for day in days_of_the_week:
statement = "Today is " + day
print statement
A range() parancs remekül használható ha a for ciklusban adott számú műveletet szeretnénk elvégezni:
for i in range(20):
print "The square of ",i," is ",i*i
A karakterláncok (string) és listák közös tulajdonsága, hogy mindkettőt lehet elemek sorozatának tekinteni. Ugyanúgy ahogy a listák elemein, a string-ek elemein is végig lehet iterálni:
for letter in "Sunday":
print letter
(Próbáld ki, hogy tudja-e a gép, hogy a szavak betűkből és nem kisnyulakból állnak össze :-)
Talán ennél is hasznosabb a szeletelés (slicing) művelet, amivel adott tartományok vághatóak ki. A legegyszerűbb szelet egyetlen elem kiválasztása az index-e alapján:
days_of_the_week[0]
Ugye emlékszünk, az indexelés 0-val (és nem 1-gyel kezdődik)! Az első két elem így választható ki:
days_of_the_week[0:2]
Figyelem: a tartomány végét jelző indexhez tartozó elem nem kerül a szeletbe. Tehát itt a lista 2-es indexű eleme, azaz a lista 3. eleme ('Tuesday') nem része. Megj.: hasonló logika alapján viselkedett a range() utasítás is, lásd fenn.
Ha 0-tól kezdődik a szelet egyszerűsítve így is írhatjuk:
days_of_the_week[:2]
Hasonlóan a lista végének indexét is elhagyhatjuk:
days_of_the_week[4:]
A lista végéről negatív indexekkel kérhetünk le értékeket:
days_of_the_week[-2:]
A kiválasztott szeletek újabb változókba tárolhatóak el:
workdays = days_of_the_week[1:6]
print workdays
Ugyanez természetesen stringekkel is megtehető:
day = "Sunday"
abbreviation = day[:3]
print abbreviation
A range() utasításhoz hasonlóan, egy újabb ':' után a tartomány indexelésének lépésköze is megadható
numbers = range(0,40)
evens = numbers[2:40:2]
evens
Ilyenkor is elhagyható a tartomány elejét és/vagy végét jelző index, ha az első elemtől vagy az utolsó elemig történik a kiválsaztás:
numbers = range(0,40)
evens = numbers[2::2]
evens
Ahhoz hogy a program reagálni tudjon bemenetekre, vagy egy már kiszámolt részeredménytől függhessen további működése, szükség van feltételek vizsgálatára. Gondoljunk például a másodfokú egyenlet megoldóképletére! Attól függően, hogy a diszkrimináns pozitív vagy negatív léteznek ill. nem léteznek valós gyökök, így értelemszerűen egy ilyen programnak "döntenie" kell. A döntés, egy feltétel vizsgálatán alapul (pl. poz. vagy neg. diszkrimináns), ami lehet igaz, vagy hamis, angolul True ill. False. Az igaz és hamis értéket tárolni képes változó típusa: boolean (Bool-változó). (Emlékezzünk vissza az int ill. float típusokra).
A feltétek vizsgálatára az angol 'ha' szócskából alkotott if utasítás szolgál:
if day == "Sunday":
print "Sleep in"
else:
print "Go to work"
(Kérdés: miért "Sleep in" (alhatunk) választ kaptunk? Mi a "day" változó értéke? Hol állítottuk be?)
Nézzük végig részletesen a fenti kódot! Az első lépés a feltétel vizsgálata, ezt önmagában is elvégezhetjük:
day == "Sunday"
A for ciklushoz hasonlóan a feltételes utasításoknál is a ':' és a tabulálás jelzi a feltételes kód blokkot. Ha a feltétel igaz ('True'), akkor a blokk végrehajtódik, ellenkező esetben, nem.
Az else (='egyébként') utasítás után lévő blokk a hamis ('False') feltétel esetén hajtódik végre. Ez a rész nem kötelező, ha hiányzik, hamis feltétel esetén egyszerűen továbblép a program.
Más típusú változókon is végezhető vizsgálat:
1 == 2
Itt jegyezzük meg, hogy a feltétek vizsgálatnál dupla egyenlőség jel szerepel ==, nem pedig az értékadáshoz használt szimpla:
1 = 2
50 == 2*25
3 < 3.14159
1 == 1.0
1 != 0
1 <= 2
1 >= 1
'ÉS' (and) ill. 'VAGY' (or) operátorokkal több logikai feltétel kombinálható:
2>1 and 5>4
1>2 or 2>1
Több összehasonlítás kombinálható így is:
hours = 5
0 < hours < 24
A fentiekben szerepelt egy érdekes vizsgálat: 1 == 1.0 ami ugyan a várt eredményt adta de az egynlőség két oldalán "különböző" szám áll, nevezetesen típusuk nem egyezik meg egyikük egész (integer) a másik valós (float) típusú. Értékük megegyezett, ezért helyes eredményt kaptunk. Van egy speciális operátor az is (kb. "az-e mint"), ami a típust is vizsgálja:
1 is 1.0
Összetett objektumok, például listák is összehasonlíthatóak. Akár a '<' ill '>' operátorok is definiálhatóak (Nézz utána!).
[1,2,3] == [1,2,4]
Ha sokfelé elágazó vizsgálatot szeretnénk végezni az ("else if") kombinációból származtatható elif használható:
if day == "Sunday":
print "Sleep in"
elif day == "Saturday":
print "Do chores"
else:
print "Go to work"
Akkor válik mindez még érdekesebbé, ha az eddig tanult iterációt és feltétel vizsgálatot kombináljuk:
for day in days_of_the_week:
statement = "Today is " + day
print statement
if day == "Sunday":
print " Sleep in"
elif day == "Saturday":
print " Do chores"
else:
print " Go to work"
A Fibonacci sequence sorozat első két eleme 0 és 1, majd a következő elemet mindig az előző kettő összegéből számoljuk ki: 0,1,1,2,3,5,8,13,21,34,55,89,...
Ha nagyobb n értékekre is ki akarjuk számolni a sorozatot, ez kiváló feladat lehet egy fáradhatatlan és gyors számítógépnek!
n = 10
sequence = [0,1]
for i in range(2,n): # This is going to be a problem if we ever set n <= 2!
sequence.append(sequence[i-1]+sequence[i-2])
print sequence
Nézzük végig lépésről lépésre! Először n értékét, azaz a kiszámolandó sorozat hosszát állítjuk be 10-re. A sorozatot majdan tároló listát sequence-nek neveztük el, és inicializáltuk az eslő két értékkel. A "kézi munka" után következhet a gép automatikus munkája, az iteráció.
Az iterációt 2-vel kezdjük (ez ugye a 0-s indexelés miatt a 3. elem lesz, hisz az első kettőt már mi megadtuk) és n-ig, a megadott lista méretig számolunk. A "#" 'megjegyzést' (angol: comment) jelöl, ezt a gép nem 'olvassa', csak a programozónak szól emlékeztetőül, vagy más programozók megértését sgíti. Noha gyakran elmarad, a kódok rendszeres dokumentálása megjegyzésekkel, később (amikor már a kód részleteit a feledés homály borítja) nagyon kifizetődő lehet.
A ciklus törzsében az addig kiszámolt lista végére hozzátűzzük (append) az előző két tag összegét. A ciklus vége után kiíratjuk az eredményt.
Ha más hosszúságú sorozatot szeretnénk átmásolhatjuk a fenti kódot egy új cellába és átírhatjuk az n=10-et pl. n=100-ra. Van azonban egy hatékonyabb módszer, új függvény-ként definiálhatjuk a def utasítás segítségével:
def fibonacci(sequence_length):
"Return the Fibonacci sequence of length *sequence_length*" # ez csak a 'help'-hez kell
sequence = [0,1]
if sequence_length < 1:
print "Fibonacci sequence only defined for length 1 or greater"
return
if 0 < sequence_length < 3:
return sequence[:sequence_length]
for i in range(2,sequence_length):
sequence.append(sequence[i-1]+sequence[i-2])
return sequence
Most már meghívhatjuk a fibonacci() függvényt különböző hosszakra:
fibonacci(2)
fibonacci(18)
Elemezzük a fenti kódot! A már megszokott módon a kettőspont és behúzás (TAB) határozza meg a függvény definícióhoz tartozó kód blokkot. A 2. sorban idézőjelek közt szerepel a "docstring", ami a függvény működését magyarázza el röviden, és később a help paranccsal hívható elő:
help(fibonacci)
Ez később nagyon hasznos lehet, érdemes megszokni, hogy dokumentáljuk munkánkat!
A függvényt megpróbáltuk "bolond-biztossá" is tenni azáltal, hogy leellenőrizzük, hogy a megadott sorozat hossz érvényes pozitív szám-e.
A függvények saját magukat is meg tudják hívni, amit rekurzió-nak nevezünk. A rekurziót a faktoriális függvényen demonstráljuk. n faktoriálisa definíció szerint:
\[ n! = n(n-1)(n-2)\cdots 1 \]
(Persze létezik beépített faktoriális függvény is, amin egyből a help utasítást is gyakorolhatjuk):
from math import factorial
help(factorial)
factorial(20)
Ha magunk akarjuk megírni a függvényt ahhoz használjuk fel, hogy:
\[ n! = n(n-1)!\]
Ezek után a program:
def fact(n):
"Calculate the factorial (n!) of a positive integer"
if n <= 0:
return 1
return n*fact(n-1)
fact(20)
A rekurzió segítségével nagyon tömör, nagyon elegáns (néha emberi ésszel már nehezen átlátható) programokat lehet írni.
Before we end the Python overview, I wanted to touch on two more data structures that are very useful (and thus very common) in Python programs.
A tuple is a sequence object like a list or a string. It's constructed by grouping a sequence of objects together with commas, either without brackets, or with parentheses:
t = (1,2,'hi',9.0)
t
Tuples are like lists, in that you can access the elements using indices:
t[1]
However, tuples are immutable, you can't append to them or change the elements of them:
t.append(7)
t[1]=77
Tuples are useful anytime you want to group different pieces of data together in an object, but don't want to create a full-fledged class (see below) for them. For example, let's say you want the Cartesian coordinates of some objects in your program. Tuples are a good way to do this:
('Bob',0.0,21.0)
Again, it's not a necessary distinction, but one way to distinguish tuples and lists is that tuples are a collection of different things, here a name, and x and y coordinates, whereas a list is a collection of similar things, like if we wanted a list of those coordinates:
positions = [
('Bob',0.0,21.0),
('Cat',2.5,13.1),
('Dog',33.0,1.2)
]
Tuples can be used when functions return more than one value. Say we wanted to compute the smallest x- and y-coordinates of the above list of objects. We could write:
def minmax(objects):
minx = 1e20 # These are set to really big numbers
miny = 1e20
for obj in objects:
name,x,y = obj
if x < minx:
minx = x
if y < miny:
miny = y
return minx,miny
x,y = minmax(positions)
print x,y
Here we did two things with tuples you haven't seen before. First, we unpacked an object into a set of named variables using tuple assignment:
>>> name,x,y = obj
We also returned multiple values (minx,miny), which were then assigned to two other variables (x,y), again by tuple assignment. This makes what would have been complicated code in C++ rather simple.
Tuple assignment is also a convenient way to swap variables:
x,y = 1,2
y,x = x,y
x,y
Dictionaries are an object called "mappings" or "associative arrays" in other languages. Whereas a list associates an integer index with a set of objects:
mylist = [1,2,9,21]
The index in a dictionary is called the key, and the corresponding dictionary entry is the value. A dictionary can use (almost) anything as the key. Whereas lists are formed with square brackets [], dictionaries use curly brackets {}:
ages = {"Rick": 46, "Bob": 86, "Fred": 21}
print "Rick's age is ",ages["Rick"]
There's also a convenient way to create dictionaries without having to quote the keys.
dict(Rick=46,Bob=86,Fred=20)
The len() command works on both tuples and dictionaries:
len(t)
len(ages)
We can generally understand trends in data by using a plotting program to chart it. Python has a wonderful plotting library called Matplotlib. The IPython notebook interface we are using for these notes has that functionality built in.
As an example, we have looked at two different functions, the Fibonacci function, and the factorial function, both of which grow faster than polynomially. Which one grows the fastest? Let's plot them. First, let's generate the Fibonacci sequence of length 20:
fibs = fibonacci(10)
Next lets generate the factorials.
facts = []
for i in range(10):
facts.append(factorial(i))
Now we use the Matplotlib function plot to compare the two.
figsize(8,6)
plot(facts,label="factorial")
plot(fibs,label="Fibonacci")
xlabel("n")
legend()
The factorial function grows much faster. In fact, you can't even see the Fibonacci sequence. It's not entirely surprising: a function where we multiply by n each iteration is bound to grow faster than one where we add (roughly) n each iteration.
Let's plot these on a semilog plot so we can see them both a little more clearly:
semilogy(facts,label="factorial")
semilogy(fibs,label="Fibonacci")
xlabel("n")
legend()
There are many more things you can do with Matplotlib. We'll be looking at some of them in the sections to come. In the meantime, if you want an idea of the different things you can do, look at the Matplotlib Gallery. Rob Johansson's IPython notebook Introduction to Matplotlib is also particularly good.
There is, of course, much more to the language than I've covered here. I've tried to keep this brief enough so that you can jump in and start using Python to simplify your life and work. My own experience in learning new things is that the information doesn't "stick" unless you try and use it for something in real life.
You will no doubt need to learn more as you go. I've listed several other good references, including the Python Tutorial and Learn Python the Hard Way. Additionally, now is a good time to start familiarizing yourself with the Python Documentation, and, in particular, the Python Language Reference.
Tim Peters, one of the earliest and most prolific Python contributors, wrote the "Zen of Python", which can be accessed via the "import this" command:
import this
No matter how experienced a programmer you are, these are words to meditate on.
Numpy contains core routines for doing fast vector, matrix, and linear algebra-type operations in Python. Scipy contains additional routines for optimization, special functions, and so on. Both contain modules written in C and Fortran so that they're as fast as possible. Together, they give Python roughly the same capability that the Matlab program offers. (In fact, if you're an experienced Matlab user, there a guide to Numpy for Matlab users just for you.)
Fundamental to both Numpy and Scipy is the ability to work with vectors and matrices. You can create vectors from lists using the array command:
array([1,2,3,4,5,6])
You can pass in a second argument to array that gives the numeric type. There are a number of types listed here that your matrix can be. Some of these are aliased to single character codes. The most common ones are 'd' (double precision floating point number), 'D' (double precision complex number), and 'i' (int32). Thus,
array([1,2,3,4,5,6],'d')
array([1,2,3,4,5,6],'D')
array([1,2,3,4,5,6],'i')
To build matrices, you can either use the array command with lists of lists:
array([[0,1],[1,0]],'d')
You can also form empty (zero) matrices of arbitrary shape (including vectors, which Numpy treats as vectors with one row), using the zeros command:
zeros((3,3),'d')
The first argument is a tuple containing the shape of the matrix, and the second is the data type argument, which follows the same conventions as in the array command. Thus, you can make row vectors:
zeros(3,'d')
zeros((1,3),'d')
or column vectors:
zeros((3,1),'d')
There's also an identity command that behaves as you'd expect:
identity(4,'d')
as well as a ones command.
The linspace command makes a linear array of points from a starting to an ending value.
linspace(0,1)
If you provide a third argument, it takes that as the number of points in the space. If you don't provide the argument, it gives a length 50 linear space.
linspace(0,1,11)
linspace is an easy way to make coordinates for plotting. Functions in the numpy library (all of which are imported into IPython notebook) can act on an entire vector (or even a matrix) of points at once. Thus,
x = linspace(0,2*pi)
sin(x)
In conjunction with matplotlib, this is a nice way to plot things:
plot(x,sin(x))
Matrix objects act sensibly when multiplied by scalars:
0.125*identity(3,'d')
as well as when you add two matrices together. (However, the matrices have to be the same shape.)
identity(2,'d') + array([[1,1],[1,2]])
Something that confuses Matlab users is that the times (*) operator give element-wise multiplication rather than matrix multiplication:
identity(2)*ones((2,2))
To get matrix multiplication, you need the dot command:
dot(identity(2),ones((2,2)))
dot can also do dot products (duh!):
v = array([3,4],'d')
sqrt(dot(v,v))
as well as matrix-vector products.
There are determinant, inverse, and transpose functions that act as you would suppose. Transpose can be abbreviated with ".T" at the end of a matrix object:
m = array([[1,2],[3,4]])
m.T
There's also a diag() function that takes a list or a vector and puts it along the diagonal of a square matrix.
diag([1,2,3,4,5])
We'll find this useful later on.
You can solve systems of linear equations using the solve command:
A = array([[1,1,1],[0,2,5],[2,5,-1]])
b = array([6,-4,27])
solve(A,b)
There are a number of routines to compute eigenvalues and eigenvectors
A = array([[13,-4],[-4,7]],'d')
eigvalsh(A)
eigh(A)
Now that we have these tools in our toolbox, we can start to do some cool stuff with it. Many of the equations we want to solve in Physics involve differential equations. We want to be able to compute the derivative of functions:
\[ y' = \frac{y(x+h)-y(x)}{h} \]
by discretizing the function \(y(x)\) on an evenly spaced set of points \(x_0, x_1, \dots, x_n\), yielding \(y_0, y_1, \dots, y_n\). Using the discretization, we can approximate the derivative by
\[ y_i' \approx \frac{y_{i+1}-y_{i-1}}{x_{i+1}-x_{i-1}} \]
We can write a derivative function in Python via
def nderiv(y,x):
"Finite difference derivative of the function f"
n = len(y)
d = zeros(n,'d') # assume double
# Use centered differences for the interior points, one-sided differences for the ends
for i in range(1,n-1):
d[i] = (y[i+1]-y[i])/(x[i+1]-x[i])
d[0] = (y[1]-y[0])/(x[1]-x[0])
d[n-1] = (y[n-1]-y[n-2])/(x[n-1]-x[n-2])
return d
Let's see whether this works for our sin example from above:
x = linspace(0,2*pi)
dsin = nderiv(sin(x),x)
plot(x,dsin,label='numerical')
plot(x,cos(x),label='analytical')
title("Comparison of numerical and analytical derivatives of sin(x)")
legend()
Pretty close!
Now that we've convinced ourselves that finite differences aren't a terrible approximation, let's see if we can use this to solve the one-dimensional harmonic oscillator.
We want to solve the time-independent Schrodinger equation
\[ -\frac{\hbar^2}{2m}\frac{\partial^2\psi(x)}{\partial x^2} + V(x)\psi(x) = E\psi(x)\]
for \(\psi(x)\) when \(V(x)=\frac{1}{2}m\omega^2x^2\) is the harmonic oscillator potential. We're going to use the standard trick to transform the differential equation into a matrix equation by multiplying both sides by \(\psi^*(x)\) and integrating over \(x\). This yields
\[ -\frac{\hbar}{2m}\int\psi(x)\frac{\partial^2}{\partial x^2}\psi(x)dx + \int\psi(x)V(x)\psi(x)dx = E\]
We will again use the finite difference approximation. The finite difference formula for the second derivative is
\[ y'' = \frac{y_{i+1}-2y_i+y_{i-1}}{x_{i+1}-x_{i-1}} \]
We can think of the first term in the Schrodinger equation as the overlap of the wave function \(\psi(x)\) with the second derivative of the wave function \(\frac{\partial^2}{\partial x^2}\psi(x)\). Given the above expression for the second derivative, we can see if we take the overlap of the states \(y_1,\dots,y_n\) with the second derivative, we will only have three points where the overlap is nonzero, at \(y_{i-1}\), \(y_i\), and \(y_{i+1}\). In matrix form, this leads to the tridiagonal Laplacian matrix, which has -2's along the diagonals, and 1's along the diagonals above and below the main diagonal.
The second term turns leads to a diagonal matrix with \(V(x_i)\) on the diagonal elements. Putting all of these pieces together, we get:
def Laplacian(x):
h = x[1]-x[0] # assume uniformly spaced points
n = len(x)
M = -2*identity(n,'d')
for i in range(1,n):
M[i,i-1] = M[i-1,i] = 1
return M/h**2
x = linspace(-3,3)
m = 1.0
ohm = 1.0
T = (-0.5/m)*Laplacian(x)
V = 0.5*(ohm**2)*(x**2)
H = T + diag(V)
E,U = eigh(H)
h = x[1]-x[0]
# Plot the Harmonic potential
plot(x,V,color='k')
for i in range(4):
# For each of the first few solutions, plot the energy level:
axhline(y=E[i],color='k',ls=":")
# as well as the eigenfunction, displaced by the energy level so they don't
# all pile up on each other:
plot(x,-U[:,i]/sqrt(h)+E[i])
title("Eigenfunctions of the Quantum Harmonic Oscillator")
xlabel("Displacement (bohr)")
ylabel("Energy (hartree)")
We've made a couple of hacks here to get the orbitals the way we want them. First, I inserted a -1 factor before the wave functions, to fix the phase of the lowest state. The phase (sign) of a quantum wave function doesn't hold any information, only the square of the wave function does, so this doesn't really change anything.
But the eigenfunctions as we generate them aren't properly normalized. The reason is that finite difference isn't a real basis in the quantum mechanical sense. It's a basis of Dirac δ functions at each point; we interpret the space betwen the points as being "filled" by the wave function, but the finite difference basis only has the solution being at the points themselves. We can fix this by dividing the eigenfunctions of our finite difference Hamiltonian by the square root of the spacing, and this gives properly normalized functions.
The solutions to the Harmonic Oscillator are supposed to be Hermite polynomials. The Wikipedia page has the HO states given by
\[\psi_n(x) = \frac{1}{\sqrt{2^n n!}} \left(\frac{m\omega}{\pi\hbar}\right)^{1/4} \exp\left(-\frac{m\omega x^2}{2\hbar}\right) H_n\left(\sqrt{\frac{m\omega}{\hbar}}x\right)\]
Let's see whether they look like those. There are some special functions in the Numpy library, and some more in Scipy. Hermite Polynomials are in Numpy:
from numpy.polynomial.hermite import Hermite
def ho_evec(x,n,m,ohm):
vec = [0]*9
vec[n] = 1
Hn = Hermite(vec)
return (1/sqrt(2**n*factorial(n)))*pow(m*ohm/pi,0.25)*exp(-0.5*m*ohm*x**2)*Hn(x*sqrt(m*ohm))
Let's compare the first function to our solution.
plot(x,ho_evec(x,0,1,1),label="Analytic")
plot(x,-U[:,0]/sqrt(h),label="Numeric")
xlabel('x (bohr)')
ylabel(r'$\psi(x)$')
title("Comparison of numeric and analytic solutions to the Harmonic Oscillator")
legend()
The agreement is almost exact.
We can use the subplot command to put multiple comparisons in different panes on a single plot:
phase_correction = [-1,1,1,-1,-1,1]
for i in range(6):
subplot(2,3,i+1)
plot(x,ho_evec(x,i,1,1),label="Analytic")
plot(x,phase_correction[i]*U[:,i]/sqrt(h),label="Numeric")
Other than phase errors (which I've corrected with a little hack: can you find it?), the agreement is pretty good, although it gets worse the higher in energy we get, in part because we used only 50 points.
The Scipy module has many more special functions:
from scipy.special import airy,jn,eval_chebyt,eval_legendre
subplot(2,2,1)
x = linspace(-1,1)
Ai,Aip,Bi,Bip = airy(x)
plot(x,Ai)
plot(x,Aip)
plot(x,Bi)
plot(x,Bip)
title("Airy functions")
subplot(2,2,2)
x = linspace(0,10)
for i in range(4):
plot(x,jn(i,x))
title("Bessel functions")
subplot(2,2,3)
x = linspace(-1,1)
for i in range(6):
plot(x,eval_chebyt(i,x))
title("Chebyshev polynomials of the first kind")
subplot(2,2,4)
x = linspace(-1,1)
for i in range(6):
plot(x,eval_legendre(i,x))
title("Legendre polynomials")
As well as Jacobi, Laguerre, Hermite polynomials, Hypergeometric functions, and many others. There's a full listing at the Scipy Special Functions Page.
Very often we deal with some data that we want to fit to some sort of expected behavior. Say we have the following:
raw_data = """\
3.1905781584582433,0.028208609537968457
4.346895074946466,0.007160804747670053
5.374732334047101,0.0046962988461934805
8.201284796573875,0.0004614473299618756
10.899357601713055,0.00005038370219939726
16.295503211991434,4.377451812785309e-7
21.82012847965739,3.0799922117601088e-9
32.48394004282656,1.524776208284536e-13
43.53319057815846,5.5012073588707224e-18"""
There's a section below on parsing CSV data. We'll steal the parser from that. For an explanation, skip ahead to that section. Otherwise, just assume that this is a way to parse that text into a numpy array that we can plot and do other analyses with.
data = []
for line in raw_data.splitlines():
words = line.split(',')
data.append(map(float,words))
data = array(data)
title("Raw Data")
xlabel("Distance")
plot(data[:,0],data[:,1],'bo')
Since we expect the data to have an exponential decay, we can plot it using a semi-log plot.
title("Raw Data")
xlabel("Distance")
semilogy(data[:,0],data[:,1],'bo')
For a pure exponential decay like this, we can fit the log of the data to a straight line. The above plot suggests this is a good approximation. Given a function \[ y = Ae^{-ax} \] \[ \log(y) = \log(A) - ax\] Thus, if we fit the log of the data versus x, we should get a straight line with slope \(a\), and an intercept that gives the constant \(A\).
There's a numpy function called polyfit that will fit data to a polynomial form. We'll use this to fit to a straight line (a polynomial of order 1)
params = polyfit(data[:,0],log(data[:,1]),1)
a = params[0]
A = exp(params[1])
Let's see whether this curve fits the data.
x = linspace(1,45)
title("Raw Data")
xlabel("Distance")
semilogy(data[:,0],data[:,1],'bo')
semilogy(x,A*exp(a*x),'b-')
If we have more complicated functions, we may not be able to get away with fitting to a simple polynomial. Consider the following data:
gauss_data = """\
-0.9902286902286903,1.4065274110372852e-19
-0.7566104566104566,2.2504438576596563e-18
-0.5117810117810118,1.9459459459459454
-0.31887271887271884,10.621621621621626
-0.250997150997151,15.891891891891893
-0.1463309463309464,23.756756756756754
-0.07267267267267263,28.135135135135133
-0.04426734426734419,29.02702702702703
-0.0015939015939017698,29.675675675675677
0.04689304689304685,29.10810810810811
0.0840994840994842,27.324324324324326
0.1700546700546699,22.216216216216214
0.370878570878571,7.540540540540545
0.5338338338338338,1.621621621621618
0.722014322014322,0.08108108108108068
0.9926849926849926,-0.08108108108108646"""
data = []
for line in gauss_data.splitlines():
words = line.split(',')
data.append(map(float,words))
data = array(data)
plot(data[:,0],data[:,1],'bo')
This data looks more Gaussian than exponential. If we wanted to, we could use polyfit for this as well, but let's use the curve_fit function from Scipy, which can fit to arbitrary functions. You can learn more using help(curve_fit).
First define a general Gaussian function to fit to.
def gauss(x,A,a): return A*exp(a*x**2)
Now fit to it using curve_fit:
from scipy.optimize import curve_fit
params,conv = curve_fit(gauss,data[:,0],data[:,1])
x = linspace(-1,1)
plot(data[:,0],data[:,1],'bo')
A,a = params
plot(x,gauss(x,A,a),'b-')
The curve_fit routine we just used is built on top of a very good general minimization capability in Scipy. You can learn more at the scipy documentation pages.
Many methods in scientific computing rely on Monte Carlo integration, where a sequence of (pseudo) random numbers are used to approximate the integral of a function. Python has good random number generators in the standard library. The random() function gives pseudorandom numbers uniformly distributed between 0 and 1:
from random import random
rands = []
for i in range(100):
rands.append(random())
plot(rands)
random() uses the Mersenne Twister algorithm, which is a highly regarded pseudorandom number generator. There are also functions to generate random integers, to randomly shuffle a list, and functions to pick random numbers from a particular distribution, like the normal distribution:
from random import gauss
grands = []
for i in range(100):
grands.append(gauss(0,1))
plot(grands)
It is generally more efficient to generate a list of random numbers all at once, particularly if you're drawing from a non-uniform distribution. Numpy has functions to generate vectors and matrices of particular types of random distributions.
plot(rand(100))
One of the first programs I ever wrote was a program to compute \(\pi\) by taking random numbers as x and y coordinates, and counting how many of them were in the unit circle. For example:
npts = 5000
xs = 2*rand(npts)-1
ys = 2*rand(npts)-1
r = xs**2+ys**2
ninside = (r<1).sum()
figsize(6,6) # make the figure square
title("Approximation to pi = %f" % (4*ninside/float(npts)))
plot(xs[r<1],ys[r<1],'b.')
plot(xs[r>1],ys[r>1],'r.')
figsize(8,6) # change the figsize back to 4x3 for the rest of the notebook
The idea behind the program is that the ratio of the area of the unit circle to the square that inscribes it is \(\pi/4\), so by counting the fraction of the random points in the square that are inside the circle, we get increasingly good estimates to \(\pi\).
The above code uses some higher level Numpy tricks to compute the radius of each point in a single line, to count how many radii are below one in a single line, and to filter the x,y points based on their radii. To be honest, I rarely write code like this: I find some of these Numpy tricks a little too cute to remember them, and I'm more likely to use a list comprehension (see below) to filter the points I want, since I can remember that.
As methods of computing \(\pi\) go, this is among the worst. A much better method is to use Leibniz's expansion of arctan(1):
\[\frac{\pi}{4} = \sum_k \frac{(-1)^k}{2*k+1}\]
n = 100
total = 0
for k in range(n):
total += pow(-1,k)/(2*k+1.0)
print 4*total
If you're interested a great method, check out Ramanujan's method. This converges so fast you really need arbitrary precision math to display enough decimal places. You can do this with the Python decimal module, if you're interested.
Integration can be hard, and sometimes it's easier to work out a definite integral using an approximation. For example, suppose we wanted to figure out the integral:
\[\int_0^\infty\exp(-x)dx=1\]
from numpy import sqrt
def f(x): return exp(-x)
x = linspace(0,10)
plot(x,exp(-x))
Scipy has a numerical integration routine quad (since sometimes numerical integration is called quadrature), that we can use for this:
from scipy.integrate import quad
quad(f,0,inf)
There are also 2d and 3d numerical integrators in Scipy. See the docs for more information.
Very often we want to use FFT techniques to help obtain the signal from noisy data. Scipy has several different options for this.
from scipy.fftpack import fft,fftfreq
npts = 4000
nplot = npts/10
t = linspace(0,120,npts)
def acc(t): return 10*sin(2*pi*2.0*t) + 5*sin(2*pi*8.0*t) + 2*rand(npts)
signal = acc(t)
FFT = abs(fft(signal))
freqs = fftfreq(npts, t[1]-t[0])
subplot(211)
plot(t[:nplot], signal[:nplot])
subplot(212)
plot(freqs,20*log10(FFT),',')
show()
There are additional signal processing routines in Scipy that you can read about here.
As more and more of our day-to-day work is being done on and through computers, we increasingly have output that one program writes, often in a text file, that we need to analyze in one way or another, and potentially feed that output into another file.
Suppose we have the following output:
myoutput = """\
@ Step Energy Delta E Gmax Grms Xrms Xmax Walltime
@ ---- ---------------- -------- -------- -------- -------- -------- --------
@ 0 -6095.12544083 0.0D+00 0.03686 0.00936 0.00000 0.00000 1391.5
@ 1 -6095.25762870 -1.3D-01 0.00732 0.00168 0.32456 0.84140 10468.0
@ 2 -6095.26325979 -5.6D-03 0.00233 0.00056 0.06294 0.14009 11963.5
@ 3 -6095.26428124 -1.0D-03 0.00109 0.00024 0.03245 0.10269 13331.9
@ 4 -6095.26463203 -3.5D-04 0.00057 0.00013 0.02737 0.09112 14710.8
@ 5 -6095.26477615 -1.4D-04 0.00043 0.00009 0.02259 0.08615 20211.1
@ 6 -6095.26482624 -5.0D-05 0.00015 0.00002 0.00831 0.03147 21726.1
@ 7 -6095.26483584 -9.6D-06 0.00021 0.00004 0.01473 0.05265 24890.5
@ 8 -6095.26484405 -8.2D-06 0.00005 0.00001 0.00555 0.01929 26448.7
@ 9 -6095.26484599 -1.9D-06 0.00003 0.00001 0.00164 0.00564 27258.1
@ 10 -6095.26484676 -7.7D-07 0.00003 0.00001 0.00161 0.00553 28155.3
@ 11 -6095.26484693 -1.8D-07 0.00002 0.00000 0.00054 0.00151 28981.7
@ 11 -6095.26484693 -1.8D-07 0.00002 0.00000 0.00054 0.00151 28981.7"""
This output actually came from a geometry optimization of a Silicon cluster using the NWChem quantum chemistry suite. At every step the program computes the energy of the molecular geometry, and then changes the geometry to minimize the computed forces, until the energy converges. I obtained this output via the unix command
% grep @ nwchem.out
since NWChem is nice enough to precede the lines that you need to monitor job progress with the '@' symbol.
We could do the entire analysis in Python; I'll show how to do this later on, but first let's focus on turning this code into a usable Python object that we can plot.
First, note that the data is entered into a multi-line string. When Python sees three quote marks """ or ''' it treats everything following as part of a single string, including newlines, tabs, and anything else, until it sees the same three quote marks (""" has to be followed by another """, and ''' has to be followed by another ''') again. This is a convenient way to quickly dump data into Python, and it also reinforces the important idea that you don't have to open a file and deal with it one line at a time. You can read everything in, and deal with it as one big chunk.
The first thing we'll do, though, is to split the big string into a list of strings, since each line corresponds to a separate piece of data. We will use the splitlines() function on the big myout string to break it into a new element every time it sees a newline () character:
lines = myoutput.splitlines()
lines
Splitting is a big concept in text processing. We used splitlines() here, and we will use the more general split() function below to split each line into whitespace-delimited words.
We now want to do three things:
For this data, we really only want the Energy column, the Gmax column (which contains the maximum gradient at each step), and perhaps the Walltime column.
Since the data is now in a list of lines, we can iterate over it:
for line in lines[2:]:
# do something with each line
words = line.split()
Let's examine what we just did: first, we used a for loop to iterate over each line. However, we skipped the first two (the lines[2:] only takes the lines starting from index 2), since lines[0] contained the title information, and lines[1] contained underscores.
We then split each line into chunks (which we're calling "words", even though in most cases they're numbers) using the string split() command. Here's what split does:
import string
help(string.split)
Here we're implicitly passing in the first argument (s, in the doctext) by calling a method .split() on a string object. In this instance, we're not passing in a sep character, which means that the function splits on whitespace. Let's see what that does to one of our lines:
lines[2].split()
This is almost exactly what we want. We just have to now pick the fields we want:
for line in lines[2:]:
# do something with each line
words = line.split()
energy = words[2]
gmax = words[4]
time = words[8]
print energy,gmax,time
This is fine for printing things out, but if we want to do something with the data, either make a calculation with it or pass it into a plotting, we need to convert the strings into regular floating point numbers. We can use the float() command for this. We also need to save it in some form. I'll do this as follows:
data = []
for line in lines[2:]:
# do something with each line
words = line.split()
energy = float(words[2])
gmax = float(words[4])
time = float(words[8])
data.append((energy,gmax,time))
data = array(data)
We now have our data in a numpy array, so we can choose columns to print:
plot(data[:,0])
xlabel('step')
ylabel('Energy (hartrees)')
title('Convergence of NWChem geometry optimization for Si cluster')
I would write the code a little more succinctly if I were doing this for myself, but this is essentially a snippet I use repeatedly.
Suppose our data was in CSV (comma separated values) format, a format that originally came from Microsoft Excel, and is increasingly used as a data interchange format in big data applications. How would we parse that?
csv = """\
-6095.12544083, 0.03686, 1391.5
-6095.25762870, 0.00732, 10468.0
-6095.26325979, 0.00233, 11963.5
-6095.26428124, 0.00109, 13331.9
-6095.26463203, 0.00057, 14710.8
-6095.26477615, 0.00043, 20211.1
-6095.26482624, 0.00015, 21726.1
-6095.26483584, 0.00021, 24890.5
-6095.26484405, 0.00005, 26448.7
-6095.26484599, 0.00003, 27258.1
-6095.26484676, 0.00003, 28155.3
-6095.26484693, 0.00002, 28981.7
-6095.26484693, 0.00002, 28981.7"""
We can do much the same as before:
data = []
for line in csv.splitlines():
words = line.split(',')
data.append(map(float,words))
data = array(data)
There are two significant changes over what we did earlier. First, I'm passing the comma character ',' into the split function, so that it breaks to a new word every time it sees a comma. Next, to simplify things a big, I'm using the map() command to repeatedly apply a single function (float()) to a list, and to return the output as a list.
help(map)
Despite the differences, the resulting plot should be the same:
plot(data[:,0])
xlabel('step')
ylabel('Energy (hartrees)')
title('Convergence of NWChem geometry optimization for Si cluster')
Hartrees (what most quantum chemistry programs use by default) are really stupid units. We really want this in kcal/mol or eV or something we use. So let's quickly replot this in terms of eV above the minimum energy, which will give us a much more useful plot:
energies = data[:,0]
minE = min(energies)
energies_eV = 27.211*(energies-minE)
plot(energies_eV)
xlabel('step')
ylabel('Energy (eV)')
title('Convergence of NWChem geometry optimization for Si cluster')
This gives us the output in a form that we can think about: 4 eV is a fairly substantial energy change (chemical bonds are roughly this magnitude of energy), and most of the energy decrease was obtained in the first geometry iteration.
We mentioned earlier that we don't have to rely on grep to pull out the relevant lines for us. The string module has a lot of useful functions we can use for this. Among them is the startswith function. For example:
lines = """\
----------------------------------------
| WALL | 0.45 | 443.61 |
----------------------------------------
@ Step Energy Delta E Gmax Grms Xrms Xmax Walltime
@ ---- ---------------- -------- -------- -------- -------- -------- --------
@ 0 -6095.12544083 0.0D+00 0.03686 0.00936 0.00000 0.00000 1391.5
ok ok
Z-matrix (autoz)
--------
""".splitlines()
for line in lines:
if line.startswith('@'):
print line
and we've successfully grabbed all of the lines that begin with the @ symbol.
The real value in a language like Python is that it makes it easy to take additional steps to analyze data in this fashion, which means you are thinking more about your data, and are more likely to see important patterns.
Strings are a big deal in most modern languages, and hopefully the previous sections helped underscore how versatile Python's string processing techniques are. We will continue this topic in this chapter.
We can print out lines in Python using the print command.
print "I have 3 errands to run"
In IPython we don't even need the print command, since it will display the last expression not assigned to a variable.
"I have 3 errands to run"
print even converts some arguments to strings for us:
a,b,c = 1,2,3
print "The variables are ",1,2,3
As versatile as this is, you typically need more freedom over the data you print out. For example, what if we want to print a bunch of data to exactly 4 decimal places? We can do this using formatted strings.
Formatted strings share a syntax with the C printf statement. We make a string that has some funny format characters in it, and then pass a bunch of variables into the string that fill out those characters in different ways.
For example,
print "Pi as a decimal = %d" % pi
print "Pi as a float = %f" % pi
print "Pi with 4 decimal places = %.4f" % pi
print "Pi with overall fixed length of 10 spaces, with 6 decimal places = %10.6f" % pi
print "Pi as in exponential format = %e" % pi
We use a percent sign in two different ways here. First, the format character itself starts with a percent sign. %d or %i are for integers, %f is for floats, %e is for numbers in exponential formats. All of the numbers can take number immediately after the percent that specifies the total spaces used to print the number. Formats with a decimal can take an additional number after a dot . to specify the number of decimal places to print.
The other use of the percent sign is after the string, to pipe a set of variables in. You can pass in multiple variables (if your formatting string supports it) by putting a tuple after the percent. Thus,
print "The variables specified earlier are %d, %d, and %d" % (a,b,c)
This is a simple formatting structure that will satisfy most of your string formatting needs. More information on different format symbols is available in the string formatting part of the standard docs.
It's worth noting that more complicated string formatting methods are in development, but I prefer this system due to its simplicity and its similarity to C formatting strings.
Recall we discussed multiline strings. We can put format characters in these as well, and fill them with the percent sign as before.
form_letter = """\
%s
Dear %s,
We regret to inform you that your product did not
ship today due to %s.
We hope to remedy this as soon as possible.
From,
Your Supplier
"""
print form_letter % ("July 1, 2013","Valued Customer Bob","alien attack")
The problem with a long block of text like this is that it's often hard to keep track of what all of the variables are supposed to stand for. There's an alternate format where you can pass a dictionary into the formatted string, and give a little bit more information to the formatted string itself. This method looks like:
form_letter = """\
%(date)s
Dear %(customer)s,
We regret to inform you that your product did not
ship today due to %(lame_excuse)s.
We hope to remedy this as soon as possible.
From,
Your Supplier
"""
print form_letter % {"date" : "July 1, 2013","customer":"Valued Customer Bob","lame_excuse":"alien attack"}
By providing a little bit more information, you're less likely to make mistakes, like referring to your customer as "alien attack".
As a scientist, you're less likely to be sending bulk mailings to a bunch of customers. But these are great methods for generating and submitting lots of similar runs, say scanning a bunch of different structures to find the optimal configuration for something.
For example, you can use the following template for NWChem input files:
nwchem_format = """
start %(jobname)s
title "%(thetitle)s"
charge %(charge)d
geometry units angstroms print xyz autosym
%(geometry)s
end
basis
* library 6-31G**
end
dft
xc %(dft_functional)s
mult %(multiplicity)d
end
task dft %(jobtype)s
"""
If you want to submit a sequence of runs to a computer somewhere, it's pretty easy to put together a little script, maybe even with some more string formatting in it:
oxygen_xy_coords = [(0,0),(0,0.1),(0.1,0),(0.1,0.1)]
charge = 0
multiplicity = 1
dft_functional = "b3lyp"
jobtype = "optimize"
geometry_template = """\
O %f %f 0.0
H 0.0 1.0 0.0
H 1.0 0.0 0.0"""
for i,xy in enumerate(oxygen_xy_coords):
thetitle = "Water run #%d" % i
jobname = "h2o-%d" % i
geometry = geometry_template % xy
print "---------"
print nwchem_format % dict(thetitle=thetitle,charge=charge,jobname=jobname,jobtype=jobtype,
geometry=geometry,dft_functional=dft_functional,multiplicity=multiplicity)
This is a very bad geometry for a water molecule, and it would be silly to run so many geometry optimizations of structures that are guaranteed to converge to the same single geometry, but you get the idea of how you can run vast numbers of simulations with a technique like this.
We used the enumerate function to loop over both the indices and the items of a sequence, which is valuable when you want a clean way of getting both. enumerate is roughly equivalent to:
def my_enumerate(seq):
l = []
for i in range(len(seq)):
l.append((i,seq[i]))
return l
my_enumerate(oxygen_xy_coords)
Although enumerate uses generators (see below) so that it doesn't have to create a big list, which makes it faster for really long sequenes.
You will recall that the linspace function can take either two arguments (for the starting and ending points):
linspace(0,1)
or it can take three arguments, for the starting point, the ending point, and the number of points:
linspace(0,1,5)
You can also pass in keywords to exclude the endpoint:
linspace(0,1,5,endpoint=False)
Right now, we only know how to specify functions that have a fixed number of arguments. We'll learn how to do the more general cases here.
If we're defining a simple version of linspace, we would start with:
def my_linspace(start,end):
npoints = 50
v = []
d = (end-start)/float(npoints-1)
for i in range(npoints):
v.append(start + i*d)
return v
my_linspace(0,1)
We can add an optional argument by specifying a default value in the argument list:
def my_linspace(start,end,npoints = 50):
v = []
d = (end-start)/float(npoints-1)
for i in range(npoints):
v.append(start + i*d)
return v
This gives exactly the same result if we don't specify anything:
my_linspace(0,1)
But also let's us override the default value with a third argument:
my_linspace(0,1,5)
We can add arbitrary keyword arguments to the function definition by putting a keyword argument **kwargs handle in:
def my_linspace(start,end,npoints=50,**kwargs):
endpoint = kwargs.get('endpoint',True)
v = []
if endpoint:
d = (end-start)/float(npoints-1)
else:
d = (end-start)/float(npoints)
for i in range(npoints):
v.append(start + i*d)
return v
my_linspace(0,1,5,endpoint=False)
What the keyword argument construction does is to take any additional keyword arguments (i.e. arguments specified by name, like "endpoint=False"), and stick them into a dictionary called "kwargs" (you can call it anything you like, but it has to be preceded by two stars). You can then grab items out of the dictionary using the get command, which also lets you specify a default value. I realize it takes a little getting used to, but it is a common construction in Python code, and you should be able to recognize it.
There's an analogous *args that dumps any additional arguments into a list called "args". Think about the range function: it can take one (the endpoint), two (starting and ending points), or three (starting, ending, and step) arguments. How would we define this?
def my_range(*args):
start = 0
step = 1
if len(args) == 1:
end = args[0]
elif len(args) == 2:
start,end = args
elif len(args) == 3:
start,end,step = args
else:
raise Exception("Unable to parse arguments")
v = []
value = start
while True:
v.append(value)
value += step
if value > end: break
return v
Note that we have defined a few new things you haven't seen before: a break statement, that allows us to exit a for loop if some conditions are met, and an exception statement, that causes the interpreter to exit with an error message. For example:
my_range()
List comprehensions are a streamlined way to make lists. They look something like a list definition, with some logic thrown in. For example:
evens1 = [2*i for i in range(10)]
print evens1
You can also put some boolean testing into the construct:
odds = [i for i in range(20) if i%2==1]
odds
Here i%2 is the remainder when i is divided by 2, so that i%2==1 is true if the number is odd. Even though this is a relative new addition to the language, it is now fairly common since it's so convenient.
iterators are a way of making virtual sequence objects. Consider if we had the nested loop structure:
for i in range(1000000):
for j in range(1000000):
Inside the main loop, we make a list of 1,000,000 integers, just to loop over them one at a time. We don't need any of the additional things that a lists gives us, like slicing or random access, we just need to go through the numbers one at a time. And we're making 1,000,000 of them.
iterators are a way around this. For example, the xrange function is the iterator version of range. This simply makes a counter that is looped through in sequence, so that the analogous loop structure would look like:
for i in xrange(1000000):
for j in xrange(1000000):
Even though we've only added two characters, we've dramatically sped up the code, because we're not making 1,000,000 big lists.
We can define our own iterators using the yield statement:
def evens_below(n):
for i in xrange(n):
if i%2 == 0:
yield i
return
for i in evens_below(9):
print i
We can always turn an iterator into a list using the list command:
list(evens_below(9))
There's a special syntax called a generator expression that looks a lot like a list comprehension:
evens_gen = (i for i in xrange(9) if i%2==0)
for i in evens_gen:
print i
A factory function is a function that returns a function. They have the fancy name lexical closure, which makes you sound really intelligent in front of your CS friends. But, despite the arcane names, factory functions can play a very practical role.
Suppose you want the Gaussian function centered at 0.5, with height 99 and width 1.0. You could write a general function.
def gauss(x,A,a,x0):
return A*exp(-a*(x-x0)**2)
But what if you need a function with only one argument, like f(x) rather than f(x,y,z,...)? You can do this with Factory Functions:
def gauss_maker(A,a,x0):
def f(x):
return A*exp(-a*(x-x0)**2)
return f
x = linspace(0,1)
g = gauss_maker(99.0,1.0,0.5)
plot(x,g(x))
Everything in Python is an object, including functions. This means that functions can be returned by other functions. (They can also be passed into other functions, which is also useful, but a topic for another discussion.) In the gauss_maker example, the g function that is output "remembers" the A, a, x0 values it was constructed with, since they're all stored in the local memory space (this is what the lexical closure really refers to) of that function.
Factories are one of the more important of the Software Design Patterns, which are a set of guidelines to follow to make high-quality, portable, readable, stable software. It's beyond the scope of the current work to go more into either factories or design patterns, but I thought I would mention them for people interested in software design.
Serialization refers to the process of outputting data (and occasionally functions) to a database or a regular file, for the purpose of using it later on. In the very early days of programming languages, this was normally done in regular text files. Python is excellent at text processing, and you probably already know enough to get started with this.
When accessing large amounts of data became important, people developed database software based around the Structured Query Language (SQL) standard. I'm not going to cover SQL here, but, if you're interested, I recommend using the sqlite3 module in the Python standard library.
As data interchange became important, the eXtensible Markup Language (XML) has emerged. XML makes data formats that are easy to write parsers for, greatly simplifying the ambiguity that sometimes arises in the process. Again, I'm not going to cover XML here, but if you're interested in learning more, look into Element Trees, now part of the Python standard library.
Python has a very general serialization format called pickle that can turn any Python object, even a function or a class, into a representation that can be written to a file and read in later. But, again, I'm not going to talk about this, since I rarely use it myself. Again, the standard library documentation for pickle is the place to go.
What I am going to talk about is a relatively recent format call JavaScript Object Notation (JSON) that has become very popular over the past few years. There's a module in the standard library for encoding and decoding JSON formats. The reason I like JSON so much is that it looks almost like Python, so that, unlike the other options, you can look at your data and edit it, use it in another program, etc.
Here's a little example:
# Data in a json format:
json_data = """\
{
"a": [1,2,3],
"b": [4,5,6],
"greeting" : "Hello"
}"""
import json
json.loads(json_data)
Ignore the little u's before the strings, these just mean the strings are in UNICODE. Your data sits in something that looks like a Python dictionary, and in a single line of code, you can load it into a Python dictionary for use later.
In the same way, you can, with a single line of code, put a bunch of variables into a dictionary, and then output to a file using json:
json.dumps({"a":[1,2,3],"b":[9,10,11],"greeting":"Hola"})
Functional programming is a very broad subject. The idea is to have a series of functions, each of which generates a new data structure from an input, without changing the input structure at all. By not modifying the input structure (something that is called not having side effects), many guarantees can be made about how independent the processes are, which can help parallelization and guarantees of program accuracy. There is a Python Functional Programming HOWTO in the standard docs that goes into more details on functional programming. I just wanted to touch on a few of the most important ideas here.
There is an operator module that has function versions of most of the Python operators. For example:
from operator import add, mul
add(1,2)
mul(3,4)
These are useful building blocks for functional programming.
The lambda operator allows us to build anonymous functions, which are simply functions that aren't defined by a normal def statement with a name. For example, a function that doubles the input is:
def doubler(x): return 2*x
doubler(17)
We could also write this as:
lambda x: 2*x
And assign it to a function separately:
another_doubler = lambda x: 2*x
another_doubler(19)
lambda is particularly convenient (as we'll see below) in passing simple functions as arguments to other functions.
map is a way to repeatedly apply a function to a list:
map(float,'1 2 3 4 5'.split())
reduce is a way to repeatedly apply a function to the first two items of the list. There already is a sum function in Python that is a reduction:
sum([1,2,3,4,5])
We can use reduce to define an analogous prod function:
def prod(l): return reduce(mul,l)
prod([1,2,3,4,5])
We've seen a lot of examples of objects in Python. We create a string object with quote marks:
mystring = "Hi there"
and we have a bunch of methods we can use on the object:
mystring.split()
mystring.startswith('Hi')
len(mystring)
Object oriented programming simply gives you the tools to define objects and methods for yourself. It's useful anytime you want to keep some data (like the characters in the string) tightly coupled to the functions that act on the data (length, split, startswith, etc.).
As an example, we're going to bundle the functions we did to make the 1d harmonic oscillator eigenfunctions with arbitrary potentials, so we can pass in a function defining that potential, some additional specifications, and get out something that can plot the orbitals, as well as do other things with them, if desired.
class Schrod1d:
"""\
Schrod1d: Solver for the one-dimensional Schrodinger equation.
"""
def __init__(self,V,start=0,end=1,npts=50,**kwargs):
m = kwargs.get('m',1.0)
self.x = linspace(start,end,npts)
self.Vx = V(self.x)
self.H = (-0.5/m)*self.laplacian() + diag(self.Vx)
return
def plot(self,*args,**kwargs):
titlestring = kwargs.get('titlestring',"Eigenfunctions of the 1d Potential")
xstring = kwargs.get('xstring',"Displacement (bohr)")
ystring = kwargs.get('ystring',"Energy (hartree)")
if not args:
args = [3]
x = self.x
E,U = eigh(self.H)
h = x[1]-x[0]
# Plot the Potential
plot(x,self.Vx,color='k')
for i in range(*args):
# For each of the first few solutions, plot the energy level:
axhline(y=E[i],color='k',ls=":")
# as well as the eigenfunction, displaced by the energy level so they don't
# all pile up on each other:
plot(x,U[:,i]/sqrt(h)+E[i])
title(titlestring)
xlabel(xstring)
ylabel(ystring)
return
def laplacian(self):
x = self.x
h = x[1]-x[0] # assume uniformly spaced points
n = len(x)
M = -2*identity(n,'d')
for i in range(1,n):
M[i,i-1] = M[i-1,i] = 1
return M/h**2
The init() function specifies what operations go on when the object is created. The self argument is the object itself, and we don't pass it in. The only required argument is the function that defines the QM potential. We can also specify additional arguments that define the numerical grid that we're going to use for the calculation.
For example, to do an infinite square well potential, we have a function that is 0 everywhere. We don't have to specify the barriers, since we'll only define the potential in the well, which means that it can't be defined anywhere else.
square_well = Schrod1d(lambda x: 0*x,m=10)
square_well.plot(4,titlestring="Square Well Potential")
We can similarly redefine the Harmonic Oscillator potential.
ho = Schrod1d(lambda x: x**2,start=-3,end=3)
ho.plot(6,titlestring="Harmonic Oscillator")
Let's define a finite well potential:
def finite_well(x,V_left=1,V_well=0,V_right=1,d_left=10,d_well=10,d_right=10):
V = zeros(x.size,'d')
for i in range(x.size):
if x[i] < d_left:
V[i] = V_left
elif x[i] > (d_left+d_well):
V[i] = V_right
else:
V[i] = V_well
return V
fw = Schrod1d(finite_well,start=0,end=30,npts=100)
fw.plot()
A triangular well:
def triangular(x,F=30): return F*x
tw = Schrod1d(triangular,m=10)
tw.plot()
Or we can combine the two, making something like a semiconductor quantum well with a top gate:
def tri_finite(x): return finite_well(x)+triangular(x,F=0.025)
tfw = Schrod1d(tri_finite,start=0,end=30,npts=100)
tfw.plot()
There's a lot of philosophy behind object oriented programming. Since I'm trying to focus on just the basics here, I won't go into them, but the internet is full of lots of resources on OO programming and theory. The best of this is contained in the Design Patterns book, which I highly recommend.
The first rule of speeding up your code is not to do it at all. As Donald Knuth said:
"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil."
The second rule of speeding up your code is to only do it if you really think you need to do it. Python has two tools to help with this process: a timing program called timeit, and a very good code profiler. We will discuss both of these tools in this section, as well as techniques to use to speed up your code once you know it's too slow.
timeit helps determine which of two similar routines is faster. Recall that some time ago we wrote a factorial routine, but also pointed out that Python had its own routine built into the math module. Is there any difference in the speed of the two? timeit helps us determine this. For example, timeit tells how long each method takes:
%timeit factorial(20)
The little % sign that we have in front of the timeit call is an example of an IPython magic function, which we don't have time to go into here, but it's just some little extra mojo that IPython adds to the functions to make it run better in the IPython environment. You can read more about it in the IPython tutorial.
In any case, the timeit function runs 3 loops, and tells us that it took on the average of 583 ns to compute 20!. In contrast:
%timeit fact(20)
the factorial function we wrote is about a factor of 10 slower. This is because the built-in factorial function is written in C code and called from Python, and the version we wrote is written in plain old Python. A Python program has a lot of stuff in it that make it nice to interact with, but all that friendliness slows down the code. In contrast, the C code is less friendly but more efficient. If you want speed with as little effort as possible, write your code in an easy to program language like Python, but dump the slow parts into a faster language like C, and call it from Python. We'll go through some tricks to do this in this section.
Profiling complements what timeit does by splitting the overall timing into the time spent in each function. It can give us a better understanding of what our program is really spending its time on.
Suppose we want to create a list of even numbers. Our first effort yields this:
def evens(n):
"Return a list of even numbers below n"
l = []
for x in range(n):
if x % 2 == 0:
l.append(x)
return l
Is this code fast enough? We find out by running the Python profiler on a longer run:
import cProfile
cProfile.run('evens(100000)')
This looks okay, 0.05 seconds isn't a huge amount of time, but looking at the profiling shows that the append function is taking almost 20% of the time. Can we do better? Let's try a list comprehension.
def evens2(n):
"Return a list of even numbers below n"
return [x for x in range(n) if x % 2 == 0]
import cProfile
cProfile.run('evens2(100000)')
By removing a small part of the code using a list comprehension, we've doubled the overall speed of the code!
It seems like range is taking a long time, still. Can we get rid of it? We can, using the xrange generator:
def evens3(n):
"Return a list of even numbers below n"
return [x for x in xrange(n) if x % 2 == 0]
import cProfile
cProfile.run('evens3(100000)')
This is where profiling can be useful. Our code now runs 3x faster by making trivial changes. We wouldn't have thought to look in these places had we not had access to easy profiling. Imagine what you would find in more complicated programs.
When we compared the fact and factorial functions, above, we noted that C routines are often faster because they're more streamlined. Once we've determined that one routine is a bottleneck for the performance of a program, we can replace it with a faster version by writing it in C. This is called extending Python, and there's a good section in the standard documents. This can be a tedious process if you have many different routines to convert. Fortunately, there are several other options.
Swig (the simplified wrapper and interface generator) is a method to generate binding not only for Python but also for Matlab, Perl, Ruby, and other scripting languages. Swig can scan the header files of a C project and generate Python binding for it. Using Swig is substantially easier than writing the routines in C.
Cython is a C-extension language. You can start by compiling a Python routine into a shared object libraries that can be imported into faster versions of the routines. You can then add additional static typing and make other restrictions to further speed the code. Cython is generally easier than using Swig.
PyPy is the easiest way of obtaining fast code. PyPy compiles Python to a subset of the Python language called RPython that can be efficiently compiled and optimized. Over a wide range of tests, PyPy is roughly 6 times faster than the standard Python Distribution.
Project Euler is a site where programming puzzles are posed that might have interested Euler. Problem 7 asks the question:
By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13.
What is the 10,001st prime number?
To solve this we need a very long list of prime numbers. First we'll make a function that uses the Sieve of Erastothenes to generate all the primes less than n.
def primes(n):
"""\
From python cookbook, returns a list of prime numbers from 2 to < n
>>> primes(2)
[2]
>>> primes(10)
[2, 3, 5, 7]
"""
if n==2: return [2]
elif n<2: return []
s=range(3,n+1,2)
mroot = n ** 0.5
half=(n+1)/2-1
i=0
m=3
while m <= mroot:
if s[i]:
j=(m*m-3)/2
s[j]=0
while j<half:
s[j]=0
j+=m
i=i+1
m=2*i+3
return [2]+[x for x in s if x]
number_to_try = 1000000
list_of_primes = primes(number_to_try)
print list_of_primes[10001]
You might think that Python is a bad choice for something like this, but, in terms of time, it really doesn't take long:
cProfile.run('primes(1000000)')
Only takes 1/4 of a second to generate a list of all the primes below 1,000,000. It would be nice if we could use the same trick to get rid of the range function, but we actually need it, since we're using the object like a list, rather than like a counter as before.
Important libraries
Other packages of interest
Thanks to Alex and Tess for everything!
Thanks to Barbara Muller and Tom Tarman for helpful suggestions.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. The work is offered for free, with the hope that it will be useful. Please consider making a donation to the John Hunter Memorial Fund.
Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under Contract DE-AC04-94AL85000.