#define a function (which actually creates a function object)
def function():
function.count += 1 # count is an attribute of the function object (defined below)
print "Function called %d times" % function.count
function.count=0 #add an attribute to the function ob
function() # --> Function called 1 times
function() # --> Function called 2 times
function() # --> Function called 3 times
Yes that is legal python code. Swallow it.
===== Global Variables =====
If a variable name is read in python all namespaces are searched in order, until a variable with that name is found. However if a variable is changed and it does not exist in the local namespace it is **created** thus shadowing a global variable with the same name.
Note that:
* this only applies to //assignment//
* Modifiying a mutable global variable (eg. adding elements to a list) is possible without declaring the variable as global
To reassign a global variable in a function it needs to be declared as **global** before it is used:
myGlobalVar = 23
def test():
global myGlobalVar
myGlobalVar=42
==== Some Global Weirdness ====
Its also interesting to note that (because of the above)
x = 3
def test():
print x
#-->Works
x=3
def test():
print x
x = 7
#-->Gives an **error** (variable used before defined).
Therefore if you want to use a global variable its best to simply put the "global myVar" line at the start of the function.
===== Modules =====
Modules can be nested in a package structure similar to java. Eg:
- src
| main.py
|----test
| |__init__.py
|--------|muh
|---- __init__.py
|MyMuh.py
The //__init__.py// files need to exist in every directory, altough they are allowed to be empty.
In order to import such a nested module the root of the directory structure (in this example the directory src) must be included in PYTHONPATH:
in main.py you have to write
import test.muh.MyMuh as muhModule
muhModule.doMuh()
===== Namespaces =====
(from [[[http://www.diveintopython.org/html_processing/locals_and_globals.html|diveintopython.org]]]:)
Namespace order # local namespace - specific to the current function or class method # global namespace - specific to the current module # built-in namespace - global to all modules
Accessing locals with the function locals() returns a copy of that namespace. Accessing globals with globals() returns the actual namespace
===== Sequences =====
==== Sorting a Sequence ====
l=[3,2,15,3,2,1,4]
lSorted = sorted(l)
Default sorting order is ascending
==== Descending Sort ====
l = [3,2,15,3,2,1,4]
lReverseSorted = sorted(l, reverse=True)
==== Sorting a list by a certain element of a list item ====
Having a sequence consisting of tuples(or sequences) like
mylist=((1,'one'),(0,'zero'),(4,'four'))
you can easily sort them by one element by using the itemgetter function:
from operator import itemgetter
mysorted = sorted(mylist,key=itemgetter(0))
(for the first part of the tuples also.)
If the list item is a class, then a lambda function has to be used:
mySorted = sorted(mylist, key = lambda element: element.myKeyAttribute)
==== Sorting a list of lists by length ====
tmp = [[[1,2,3],[3],[6,7]]]
sorted(tmp, lambda x,y: len(x)-len(y))
==== Sorting values in a Dict ====
import operator
items = sorted(my_dict.items(), key=operator.itemgetter(1)) # sort by value, itemgetter(0) to sort by key
==== List intersection/union/difference ====
Union A∪B
union=A+filter(lambda x:x not in A,B)
Intersection A∩B
intersection=filter(lambda x:x in A,B)
Difference A\B
difference=filter(lambda x:x not in B,A)
Symmetrical difference AΔB
symdifference=filter(lambda x:x not in B,A)+filter(lambda x:x not in A,B)
==== List comprehension ====
An easy way to define lists
noprimes = [j for i in range(2, 8) for j in range(i*2, 50, i)]
primes = [x for x in range(2, 50) if x not in noprimes]
==== Subtracting two lists ====
To element-wise subtract two lists from each other
# define lists x=[5,6,7]
y=[3,4,5]
# subtract them element-wise
import operator
map(operator.sub, x, y)
>>> [2,2,2]
==== Flattening lists ====
l = [[[1,2,3],[4,5,6], [7], [8,9]]]
[item for sublist in l for item in sublist]
==== Iterating ====
The docs of [[https://docs.python.org/2/library/itertools.html#recipes|itertools]] have lots of useful recipes.
Favourites:
from itertools import tee, izip
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = tee(iterable)
next(b, None)
return izip(a, b)
l = range(4)
for i in pairwise(l):
print i
===== Dict comprehension =====
dict([(i, chr(65+i)) for i in range(4)])
===== Merging Sequences =====
def merge(*input):
return reduce(list.add, input, list())
Using:
a=[1,3,4]
b=[5,6]
c=[[a","b","c]]
merge(a,b,c) # [1, 3, 4, 5, 6, 'a', 'b', 'c']
===== Tuples =====
==== "Modifying" a Tuple ====
Tuples are immutable and can not be modified. In order to change one element in a tuple, a new tuple has to be constructed. For example to add 36 to the 4th element of the tuple:
t=(1,2,3,4,5,6,7,8,9)
t1 = (t[:3] + (t[3]+36,) + t[4:])
Or simply create a list from the tuple
t=(1,2,3,4,5,6,7,8,9)
t1 = list (t)
t1[3] += 36
===== Lambda functions =====
Lambda functions are inline functions with a simplified syntax. They are best used for functionality which is not really reusable in other parts of the code. See [[http://www.diveintopython.org/power_of_introspection/lambda_functions.html|diveintopython.org]] for more.
g = lambda x: x*2
# g(3) will return 6
===== and or =====
Those two keywords act boolean but they return one of the values they compare. Evaluation is from left to right.
x = 12 if (y>0) else -12
==== Ternary Operator pre Python 2.5 ====
In **older Python versions** and/or can be used to emulate the ternary operator
1 and "first" or "second" # returns first
However since "" is false in a boolean context
1 and "" or "second" # returns second
so **be careful**!
A safe way to emulate the ternary operator if?then:else is
result = ((c>3) and [a] or [b])[0] # if c>3 return a else return b
See [[[http://diveintopython.org/power_of_introspection/and_or.html|diveintopython.org]]] for more.
===== Reloading modules =====
import qgisfleettools as q
q.doStuff()
reload(q)
===== String Formatting =====
==== Recommended approach ====
Use [[http://docs.python.org/2/library/stdtypes.html#str.format|str.format()]]. Fields can be replaced by index or by name:
print '{1} and {0}'.format('spam', 'eggs') # by index
print 'This {food} is {adjective}.'.format(food='spam', adjective='absolutely horrible') # by name
print 'The story of {0}, {1}, and {other}.'.format('Bill', 'Manfred', other='Georg') # both
==== Old approach: % operator ====
//* By Position //
" %s , %d , %f " % (a_string,an_int, a_float)
//* By name //
" %(name1)s %(name2)d %(name1)s" % {"name1":value1, "name2":value2} #note that name1 is used several times in the template!
//* The "locals trick" The function ''locals()'' creates a dict from all variables in the namespace. This can be used for easier "by-name" variable substitution: //
y=7
print ("value = %(y)s" % locals() )
==== Formatting Numbers ====
i = 4
"%d" % (i,) # --> "4"
"%4d" % (i,) # --> " 4"
"%04d" % (i,) # --> "0004"
j = 1.3 "%.2f" % (j) # --> "1.30" ''
==== Templates ====
String templates provide a simpler string way for substitutions. Instead of the normal "%"-based substitutions, Templates support "$"-based substitution
from string import Template
s = Template('$who likes $what')
s.substitute(who='tim', what='kung pao')
see http://www.python.org/doc/2.5.2/lib/node40.html for details
==== Rot 13 ====
"muh".encode('rot13')
===== Classes =====
==== Automatically set parameters of initializer as member variables ====
This may go against the zen of python (explicit is better than implicit) but it is extremely convenient when dealing with a lot of arguments in the initializer
class MyClassWithLongInitializer:
def __init__(self, a,b,c,d,e,f,g, x, y=244):
self.__dict__.update(**locals()) # instead of self.a=a;self.b=b;self.c=c ...
==== Taking care of boilerplate code ====
[[https://glyph.twistedmatrix.com/2016/08/attrs.html|attrs]] helps with boilerplate code a lot, e.g. initialisation, string representation, comparison.
import attr
@attr.s
class Point3D(object):
x = attr.ib()
y = attr.ib()
z = attr.ib()
====== CGI Scripting ======
===== Content Type =====
Is set simply by printing the corresponding information at the start of the script:
import cgi
print "Content-Type: text/html\n" # or "Content-Type: image/png\n" or somesuch
===== Get Request Parameters =====
import cgi
sectionId = cgi.FieldStorage()['sectionid'].value
see http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/81547 for a script to create a dict from the fieldStorage
===== Show errors in output page =====
The cgitb module provides a special exception handler for Python scripts. (Its name is a bit misleading. It was originally designed to display extensive traceback information in HTML for CGI scripts. It was later generalized to also display this information in plain text.) After this module is activated, if an uncaught exception occurs, a detailed, formatted report will be displayed. The report includes a traceback showing excerpts of the source code for each level, as well as the values of the arguments and local variables to currently running functions, to help you debug the problem. ( text shamelessly stolen from http://docs.python.org/lib/module-cgitb.html )
import cgitb
cgitb.enable()
====== Commandline Parameters ======
===== Simple =====
import sys
print sys.argv[1]
===== Advanced =====
I have found several builtin modules to deal with commandline parameters. The most flexible and object oriented seems to be OptionParser.
==== Option Parser ====
from optparse import OptionParser
parser= OptionParser("usage: %prog [options] INPUT_FILE")
parser.add_option("-f", "--file", dest="infile", help="input file")
parser.add_option("-d", "--direction", choices=[[0,1]], dest="direction", help="direction of the road")
parser.add_option("-v", "--verbose", dest="verbosity", action="count",default=0, help="Increase Verbosity of debugging output: -v -vv -vvv ")
parser.add_option("-s", "--show-invalid-lines", dest="showInvalidLines", action="store_true",default=False, help="shows...")
(options,args) = parser.parse_args("test -v --file out.txt".split())
if (options.showInvalidLines):
print "Showing Invalid Lines"
if (options.verbosity > 1):
print "Very Verbose"
However, optparse is deprecated since 2.7, the (very similar) replacement is argparse: [[http://docs.python.org/2/library/argparse.html#module-argparse]]
====== Compressed Data ======
===== Gzip =====
Reading
import gzip
f = gzip.open('file.txt.gz')
file_content = f.read()
f.close()
Writing
import gzip
f_out = gzip.open('file.txt.gz', 'wb')
f_out.write(data)
f_out.close()
====== Config Files ======
There appear to be several ways to read in config files. This example uses the ConfigParser class.
===== Config File Layout =====
Here is an example config file
[DEFAULT]
text:"Das ist ein text"
[General]
muh:"Die Kuh macht muh"
times:3
The DEFAULT Section is special, the names of other Sections can be whatever you like.
===== Reading the config file =====
from ConfigParser import ConfigParser
config = ConfigParser()
config.read("test.cfg")
print config.get("General","muh")
config.getint("General", "times")
print config.get("General", "text") # text not defined in General section, but a DEFAULT definition exists
myBool = config.getboolean("General", "myBoolean")
===== Writing a config file =====
from ConfigParser import ConfigParser
config = ConfigParser()
config.add_section("Test")
config.set("Test", "Muh", 123)
with open('example.cfg', 'wb') as configfile:
config.write(configfile)
====== Cryptography ======
===== Hashes =====
import hashlib
m = hashlib.sha512()
m.update("text")
m.update("more text")
print m.hexdigest()
See http://www.python.org/doc/current/lib/module-hashlib.html for details and a list of available hash algorithms.
====== CSV Files ======
===== Reading CSV Files =====
A CSV file with a header can be read like so:
import csv
for row in csv.DictReader(file(file_name), delimiter=";"):
print row["id"]
===== Writing CSV Files =====
CSVs with headers can also be written with the csv library:
import csv
with open('names.csv', 'w') as csvfile:
fieldnames = ['first_name', 'last_name']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
writer.writerow({'first_name': 'Baked', 'last_name': 'Beans'})
writer.writerow({'first_name': 'Lovely', 'last_name': 'Spam'})
writer.writerow({'first_name': 'Wonderful', 'last_name': 'Spam'})
====== Database ======
===== Server-side cursors =====
[[http://en.wikipedia.org/wiki/Cursor_%28databases%29|Database cursors]] are a construct to traverse over records.
When data is selected, it usually gets transferred to the client process first - the cursor is on the **client side**.
For large result sets this poses an obvious problem if the client lacks the required resources. Some drivers support **server-side cursors**. Using those, the client can control how much data it wants to receive at once, thus being able to handle even very large datasets.
For recipes see
* [[python_cookbook#postgresql_server-side_cursor| PostgreSQL]]
===== Postgres =====
sudo apt-get install python-psycopg2
==== Selecting ====
import psycopg2
conn = psycopg2.connect(host="localhost", database="mydb", user="soma", password="xxx")
cursor = conn.cursor()
cursor.execute("select * from timeseries limit 10")
for row in cursor:
print row
Using column names to index rows:
import psycopg2
import psycopg2.extras
conn = psycopg2.connect(host="localhost", database="mydb", user="soma", password="xxx")
dict_cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)
dict_cur.execute("SELECT a,b,c FROM table")
for r in cursor:
print r["a"]
* see http://initd.org/psycopg/docs/extras.html
==== PostgreSQL server-side cursor ====
In order to use server-side cursors with PostgreSQL/psycopg2 one just needs to use psycopg2's **named cursor** and set an adequate [[http://initd.org/psycopg/docs/cursor.html#fetch|itersize]]
cursor = conn.cursor(name='a_cursor')
cursor.itersize = 10000
for row in cursor:
pass
For details see e.g. [[http://initd.org/psycopg/docs/usage.html#server-side-cursors|psycopg2's server-side doc on cursors]] and the [[http://www.postgresql.org/docs/current/static/sql-declare.html| PostgreSQL cursor doc]]
==== Batch insert ====
import psycopg2
conn = psycopg2.connect(host="localhost", database="mydb", user="soma", password="xxx")
cursor = conn.cursor()
INSERT = "INSERT INTO mytable (a,b,c) values (%s,%s,%s)"
data = [(12,'meas',14),(14,'est',11)]
cursor.executemany(INSERT, data)
==== Select into numpy Array ====
import numpy
import psycopg2 as pgdb
...
cursor = conn.cursor()
cursor.execute('SELECT a, b FROM demo')
result = numpy.fromiter((tuple (row) for row in cursor), dtype=[('a',float), ('b', float)], count = cursor.rowcount)
I had some trouble getting this to work with datetime. Here is a workaround (not efficient, but working)
import dateutil
import numpy
...
cursor.execute('SELECT time, value FROM table') # query
data = [(dateutil.parser.parse(time),value) for (time,value) in cur] # convert time column to datetime objects
result = numpy.array(data, dtype=[('time',object), ('value', float)]) # convert to numpy array
==== Inserting ====
import psycopg2
conn = psycopg2.connect(host="localhost", database="mydb", user="soma", password="xxx")
cursor = conn.cursor()
try:
cursor.execute( "INSERT INTO timeseries_valuetype (name, unit) VALUES ( %(name)s, %(unit)s )", {"name":"mph", "unit":"mp/h"});
conn.commit()
except pgdb.DatabaseError, details:
print "Got a DatabaseError, details are: " + str(details)
conn.rollback()
Note:The parameter (%(name)s ) for this style of placeholder variable is a dictionary that maps the names of the placeholders in the first part to values. The s indicates type string.
==== Copy ====
The psycopg2 driver offers superior batch inserting performance using the copy_from method:
import psycopg2
import cStringIO
# create data to copy (basically a csv file)
data = cStringIO.StringIO() # use cStringIO for best performance
data.write(u"1,1000\n")
data.write(u"2,2000\n")
data.seek(0) # jump to beginning of "file"
conn = psycopg2.connect(host="localhost", database="mydb", user="soma", password="xxx")
cursor.copy_from(data, 'my_table', sep=",") # copy data to my_table
conn.commit()
==== Tutorials ====
http://homepages.inf.ed.ac.uk/s9808248/ad/tutorial7.php
===== SQLite =====
sudo apt-get install python-pysqlite2 sqlite3
**Note:** python-pysqlite2 creates sqlite3 databases. SQLite2 and SQLite3 are not compatible!
==== Creating a DB Connection ====
from pysqlite2 import dbapi2 as sqlite
connection = sqlite.connect("test.db") # or use :memory: to create an in-memory database
cursor = connection.cursor()
==== Attaching a second DB ====
cursor.execute("attach '/var/fleet/output/trips.db' as trips")
==== Selecting ====
cursor.execute("SELECT * from names")
print cursor.fetchall()
#
# or
#
for row in cursor:
print row[0]
connection.close()
==== Inserting ====
cursor.execute ('CREATE TABLE names (id INTEGER PRIMARY KEY, name VARCHAR(50), email VARCHAR(50))')
cursor.execute('INSERT INTO names VALUES (null, "John Doe", " jdoe@jdoe.zz ")')
print cursor.lastrowid
connection.commit()
Batch insert
cursor.execute( 'create table test(roadid INTEGER, name TEXT)')
values = [(1,'one'),(2,'two')] # anything iterable will do
cursor.executemany("INSERT INTO test (roadid, name) VALUES (?,?) ", values)
==== Creating user defined functions ====
see: [[[http://koeritz.com/docs/python-pysqlite2/usage-guide.html#native-database-engine-features-and-extensions-beyond-the-python-db-api|PySqlite usage guide]]]
==== Aggregate Function ====
from pysqlite2 import dbapi2 as sqlite
class MySum:
def __init__(self):
self.count = 0
def step(self, value):
self.count += value
def finalize(self):
return self.count
con = sqlite.connect(":memory:")
con.create_aggregate("mysum", 1, MySum)
cur = con.cursor()
cur.execute("create table test(i)")
cur.execute("insert into test(i) values (1)")
cur.execute("insert into test(i) values (2)")
cur.execute("select mysum(i) from test")
print cur.fetchone()[0]
==== load_extension ====
According to [[[http://code.google.com/p/xenia/wiki/SpatialLite|documentation]]] it is necessary to explicitly enable load_extension
DB = sqlite.connect( './html_content.db' )
DB.enable_load_extension(True)
DB.execute( "SELECT load_extension('/usr/lib/libspatialite.so.2.0.3')" )
===== MSSQL =====
The best way I found so far is to use [[http://pymssql.sourceforge.net/ pymssql]]. Unfortunately there is currently no ubuntu package for pymssql so it needs to be installed by hand. Luckily this is quite trivial:
==== Install pymssql ====
Start by installing the required dependencies
sudo aptitude install python2.5-dev freetds-dev
Download and the latest pymssql release from [[https://sourceforge.net/project/showfiles.php?group_id=40059 pymssql]] at sourceforge and install it:
tar -xvzf pymssql-0.8.0.tar.gz cd pymssql-0.8.0 python setup.py install
==== Using pymssql ====
A short usage example stolen from [[http://john.parnefjord.se/node/43 here]]
import _mssql
mssql=_mssql.connect('mssql.server.com','databaseuser','password')
mssql.select_db('Northwind')
query="select firstname,lastname,birthdate from dbo.Employees;"
if mssql.query(query):
rows=mssql.fetch_array()
rowNumbers = rows[0][1]
print "Number of rows fetched: " + str(rowNumbers)
for row in rows:
for i in range(rowNumbers):
print str(i) + "\t" + row[2][i][0] + "\t" + row[2][i][1] + "\t" + str(row[2][i][2])
else:
print mssql.errmsg() print mssql.stdmsg()
mssql.close()
===== MYSQL =====
A good introduction to mysql-python can be found at http://mysql-python.sourceforge.net/MySQLdb.html#mysqldb
==== Install ====
sudo apt-get install python-mysqldb
==== Select ====
import MySQLdb
connection = MySQLdb.connect("10.101.21.25", "user","pass","database")
cur = connection.cursor()
cursor.execute("SELECT * from timeseries")
result = cur.fetchall()
for r in result:
print r
cur.close()
connection.close()
==== Insert ====
import MySQLdb
connection = MySQLdb.connect("10.101.21.25", "user","pass","database")
cur = connection.cursor()
cur.execute(""" INSERT INTO timeseries(day,roadid,laneid,speed) values ('2008-01-01', 1,1,60.0) """)
connection.commit()
==== Batch Insert ====
c.executemany( """INSERT INTO breakfast (name, spam, eggs, sausage, price) VALUES (%s, %s, %s, %s, %s)""",[
("Spam and Sausage Lover's Plate", 5, 1, 8, 7.95 ),
("Not So Much Spam Plate", 3, 2, 0, 3.95 ),
("Don't Wany ANY SPAM! Plate", 0, 4, 3, 5.95 )
] )
Notes: * there is a mix of types in the values array (strings, ints, floats) but we still only use %s in the format string (otherwise you will get an error!) * executemany() tries to throw the whole values array at MySQL at once. If you try to insert many thousand records, this may //exceed MySQL's standard buffer size//, and wil give you an exception:
_mysql_exceptions.OperationalError: (1153, "Got a packet bigger than 'max_allowed_packet' bytes")
To prevent this you need to manually split your value-list into smaller batches like this:
batch_size=20000 # you might have to experiment to find optimal batch_size for your data
while values: # repeat until all records in values have been inserted ''
batch, values = values[:batch_size], values[batch_size:] #split values into the current batch and the remaining records
cur.executemany("INSERT INTO timeseries(day,roadid,laneid,intrvl,speed,stddev,count) VALUES (%s,%s,%s,%s, %s, %s, %s)", batch ) #insert current batch ''
====== Date and time ======
There are (at least) two separate ways to deal with dates:
* 'time' - represents a timestamp as a tuple of at least 9 values and may be deprecated by now. (see [[http://pleac.sourceforge.net/pleac_python/datesandtimes.html | here ]] )
* 'datetime' - represents a timestamp as a datetime object ( [[http://docs.python.org/lib/datetime-datetime.html|documentation]] )
===== Get Current Date/Time =====
import datetime
current_time = datetime.datetime.now()
===== Parse dates =====
Python provides a dateutil library which can be used to parse many common date formats:
import dateutil.parser
dateutil.parser.parse("2011-05-18 12:30:00")
If you need to parse a custom format, use the strptime function of the datetime library ([[http://docs.python.org/library/datetime.html#strftime-strptime-behavior|check here for directives]] and their meanings):
import datetime
d = datetime.datetime.strptime("20071031T235958","%Y%m%dT%H%M%S")
year = d.year # access fields of datetime
the other is to use the time library (which seems a little less intuitive)
import time
year,month,day = time.strptime("20071003","%Y%m%d")[0:3] # values returned as tuple of ints print year,month,day h,m,s=time.strptime("04:12:02", "%H:%M:%S")[3:6]
To handle ISO 8601 timestamps like 20071031T235958 and get a datetime object:
datetime.datetime.strptime("20071031T235958","%Y%m%dT%H%M%S")
Get Unix timestamp from Python datetime:
calendar.timegm(tuple)
Get Python time from Unix timestamp:
time.gmtime(unixtimestamp)
===== Date formatting =====
Print datetime object
dat= datetime.datetime.strptime('2008-04-21 11:00:00', '%Y-%m-%d %H:%M:%S')
dat.strftime('%Y%m%dT%H%M%S')
To print a time object prettily
time.strftime("%Y-%m-%d %H:%M:%S",timeStamp)
===== Manipulating Dates =====
import datetime
d = datetime.datetime(2007,11,21,12,33)
d += datetime.timedelta(days=4,hours=2,minutes=40,seconds=20,milliseconds=300);
x=datetime.now()
x.replace(minute=20)
===== Difference of 2 Dates =====
Getting the difference between 2 datetime-objects is easy:
testTime1=datetime.datetime(2001,1,1,0,0,0)
testTime2=datetime.datetime(2009,4,23,12,34,45)
difference=testTime2-testTime1
print difference #the effect of these few lines: 3034 days, 12:34:45
But you also can split this difference into weeks,days,minutes,hours,...
weeks, days = divmod(difference.days, 7)
minutes, seconds = divmod(difference.seconds, 60)
hours, minutes = divmod(minutes, 60)
===== Timezones =====
As far as I know datetime.strptime ignores the timezone information, and **always creates naive (timezone-unaware) datetimes**. Often this is not what you want. In order to attach a timezone to a naive timestamp, and then convert it to a local time use the following:
sudo aptitude install python-tz
==== Attach Timezone to a "dumb" timestamp ====
import pytz
import datetime
ts = datetime.datetime.now() # create a dumb timestamp
tz_vienna = pytz.timezone("Europe/Vienna")
localized_ts = tz_vienna.localize(ts) # this just assumes that the timestamp is "right" and attaches the timezone. But it DOES correctly handle daylight savings time
==== Convert a Time ====
import datetime
import pytz
# create naive timestamp
naive_time = datetime.datetime.strptime("24.11.11 12:46:25", "%d.%m.%y %H:%M:%S")
# attach timezone
tz_vienna = pytz.timezone("Europe/Vienna")
local_time = tz_vienna.localize(naive_time)
# convert to utc
utc_time = local_time.astimezone(pytz.utc)
====== External Commands ======
To simply run an external command.
import os
exitValue = os.system("ls")
Note that this returns the commands **exit code shifted by 8 bits**! (dont ask.. read http://blog.tsul.net/2008/04/ossystem-and-its-return-value.html)
If you want the programs exit code it is probably easier to do
import subprocess
exitCode = subprocess.call([[ls","-a]])
Passing a list with pieces of commandline is often not very handy. Using call like so
import subprocess
exitCode = subprocess.call("ls -a", shell=True)
means (surprise!):
> "the specified command will be executed through the shell. This can be useful if you are using Python primarily for the enhanced control flow it offers over most system shells and still want access to other shell features such as filename wildcards, shell pipes and environment variable expansion."
It also means a **SECURITY HAZARD if the input to the command comes from untrustable sources!** (from http://docs.python.org/dev/library/subprocess.html#frequently-used-arguments )
So use with care.
To execute an external command and **get the output** use
import commands
output = commands.getoutput("grep 'muh' input.txt")
====== File handling ======
===== Reading =====
==== Reading file line per line ====
(from [[[http://www.yukoncollege.yk.ca/~ttopper/COMP118/rCheatSheet.html|here]]] : Remember that **line is a string** even if it looks like a number)
infile = open( infilename, "r" )
for line in infile:
# Do stuff with line. # e.g. num = int( line )
infile.close()
==== Filter input lines in finite time ====
Reading in a whole file and filtering afterwards is rather slow. For now it's using grep because I have not found out how speed optimization can be done otherwise. Giving grep the -s flag prevents us from having grep's error message in our lines list. We can thus assume that we either have the expected lines or none at all.
import commands
lines = commands.getoutput("grep -s " + myregex + " " + myfile).strip().split("\n") # we have a list of the lines that matched the regular expressions now
for line in lines:
if(len(line)>0):
#do something
===== Writing =====
f = file("test.txt","w")
f.write("muh")
f.close()
The mode string can be "w" (write) or "a"(append) , if binary data needs to be written use "wb" or "ab"
===== Files with Umlauts =====
One way to handle nasty Umlauts: (check [[ http://docs.python.org/library/codecs.html | here ]] to find the correct codec)
import codecs
inputfile = codecs.open("something.csv", "r","latin1")
for i, line in enumerate(inputfile):
print line
inputfile.close()
outputfile = codecs.open("somethingelse.csv","w","latin1")
outputfile.write("Mäh, öh, blüb!")
outputfile.close() ''
Another way:
fileencoding = "iso-8859-1"
for raw in file("Taxistandplaetze(Sektoren).csv"):
print raw.decode(fileencoding)
Convert string to another encoding:
import codecs
inputfile = codecs.open("something.csv", "r","latin1")
line = inputfile.next()
ascii_line = line.encode("ascii","ignore")
# if a character can not be encoded in this encoding python would normally raise an exception
# 'ignore' tells the encoder to ignore such errors. other options are 'replace' , 'xmlcharrefreplace', 'backslashreplace'
# see http://docs.python.org/library/codecs.html
===== File number support =====
(Found on a mailing list)
from itertools import izip, count
def enumerate(iterable, start=0):
return izip(count(start), iterable) # redefine enumerate
for i, line in enumerate(infile):
print "line number: " + str(i) + ": " + line.rstrip()
===== Replace lines in-line =====
import fileinput
for line in fileinput.input(onefilename, inplace=1):
print line.replace(old,new)
===== Checking if a File/Directory Exists =====
import os
os.path.exists("/path/to/some/where")
===== Get filename from absolute path =====
import os
path,filename = os.path.split(absolute_path)
===== Creating Directories =====
import os
if not os.path.isdir(dir):
os.makedirs(dir) # creates all non-existent directories in the path of dir
===== Working with the Working-Directory =====
(yes its a bad pun, I know)
import os
current_work_dir = os.getcwd() #get current working dir
os.chdir(new_work_dir) #change working dir ''
If your script needs to change the working directory I would strongly suggest not to rely on relative paths. Use os.getcwd() to store the script's base path and construct absolute paths for every os.chdir() you are going to do!
===== Deleting a Directory =====
Deleting an empty directory can be done with os.rmdir() but most of the time your directory will not be empty. If you want to do the equivalent of an //rm -rf dir_to_delete// use
import shutil
shutil.rmtree("dir_to_delete")
===== Listing Directory Contents =====
If you want to use wildcards try:
import glob
dir_contents = glob.glob("/home/soma/*.txt")
An alternative would be to use os.listdir. The following script lists all files in the current directory (it filters out subdirectories)
import os
dirContents = os.listdir(".")
for aFile in (c for c in dirContents if not os.path.isdir(c)):
print "a file: %s", aFile
===== Temporary Files/Directories =====
import tempfile
tempfile.mkstemp() # create temporary file
tempfile.mkdtemp(".tmp", "backup_") #create a temp directory with prefix and suffix. eg: /tmp/backup_rt5JWA.tmp
====== JSON-RPC ======
For a json-rpc library take a look at http://json-rpc.org/ . But be warned right now the ServiceProxy does not set the content-type and might not work with some servers.
===== Client =====
Using the python json library it is very easy to create a simple ServiceProxy client (code mostly stolen from json-rpc.org but added content-type. I am posting this here because the client is so simple that installing an extra library might be overkill).
# Code mostly from http://json-rpc.org/
import urllib2
import json
# Define helper classes
class JSONRPCException(Exception):
def __init__(self, rpcError):
Exception.__init__(self)
self.error = rpcError
class ServiceProxy(object):
def __init__(self, serviceURL, serviceName=None):
self.__serviceURL = serviceURL
self.__serviceName = serviceName
def __getattr__(self, name):
if self.__serviceName != None:
name = "%s.%s" % (self.__serviceName, name)
return ServiceProxy(self.__serviceURL, name)
def __call__(self, *args):
postdata = json.dumps({"method": self.__serviceName, 'params': args, 'id':'jsonrpc'})
req = urllib2.Request(url=self.__serviceURL, data=postdata)
req.add_header("Content-Type", "application/json")
respdata = urllib2.urlopen(req).read()
resp = json.loads(respdata)
if resp['error'] != None:
raise JSONRPCException(resp['error'])
else:
return resp['result']
# Usage Example
sp = ServiceProxy('http://some-jsonrpc-service.com/service')
result = sp.someMethod(1,"two",3)
====== Logging ======
===== Logging to Console =====
# Initialize the Logger
import logging
logging.basicConfig(level=logging.DEBUG, format="%(asctime)-15s\t%(name)-5s\t%(levelname)-8s\t%(message)s")
logging.info("Logging Initialized")
===== Logging to File =====
import logging
logging.basicConfig(filename="mylog.log", level=20, format="%(asctime)-15s %(levelname)s\t(%(filename)s:%(lineno)d) - %(message)s")
logging.info("Logging Initialized.")
see also:
* [[http://docs.python.org/lib/module-logging.html|Logger Documentation]]
* [[http://docs.python.org/release/2.5/lib/node422.html|Python 2 output Formatting]]
* [[https://docs.python.org/3/howto/logging-cookbook.html|Python 3 logging cookbook]]
====== Mathematics ======
===== Rounding =====
import math
print math.floor(2.4)# --> 2.0
print math.ceil(2.4) # --> 3.0
print round(2.4) # --> 2
print int(2.4) # --> 2
print round(2.6) # --> 3
print int(2.6) # --> 2 Conversion to int **truncates** the comma, the round function rounds!
# rounding errors
round(26.9403314917,2) # --> 26.940000000000001
myvar = round(26.9403314917,2)
print myvar # --> gives 26.94, which is a good enough estimation in most cases
===== int to bin =====
From [[http://www.daniweb.com/code/snippet285.html|here]]:
count=24
n=286476
print "".join([str((n >> y) & 1) for y in range(count-1, -1, -1)])
===== Transpose of a matrix =====
Entries are stored row by row in list of lists (or tuples)
>>> x = [[1, 2, 3], [4, 5, 6]]
>>> zip(*x) [(1, 4), (2, 5), (3, 6)]
====== Machine Learning ======
Interesting libraries
* http://mlpy.sourceforge.net/
* http://www.pymvpa.org/
* http://scikit-learn.sourceforge.net/
===== k-means clustering =====
**Attention:** Make sure your data does not contain NANs!
import pylab
import numpy
import scipy.cluster.vq
# the data to cluster
timeseries = numpy.array([numpy.random.rand(10) for i in range(10)])
# find 5 centroids
centroids, x = scipy.cluster.vq.kmeans(timeseries, 5)
# assign each timeseries to a centroid
idx,_ = scipy.cluster.vq.vq(timeseries,centroids)
# plot centroids and corresponding timeseries
pylab.figure()
for i in range(5):
pylab.subplot(510+i)
cluster = i
pylab.plot(timeseries[idx==cluster].T, color='b')
pylab.plot(centroids[cluster].T, color='r')
===== Hierarchical clustering =====
**Attention:** Make sure your data does not contain NANs!
import pylab
import numpy
import scipy.cluster.hierarchy as hcluster
timeseries = numpy.array([numpy.random.rand(10) for i in range(50)])
# get a cluster-id for every timeseries
idx = hcluster.fclusterdata(timeseries, 4.0, criterion='maxclust', method='complete')
# calculate the centroid for every cluster
centroids={}
for i in set(idx):
centroid = pylab.mean(timeseries[idx==i],0)
centroids[i] = centroid
# plot centroids and corresponding timeseries
fig = pylab.figure()
for i in range(0, len(centroids)):
subplot = len(centroids)*100 + 10 + i+1
pylab.subplot(subplot)
cluster = i+1
pylab.plot(centroids[cluster], color='r', linewidth=2)
pylab.plot(timeseries[idx==cluster].T, color='b')
pylab.axis(ymin=0)
===== PCA =====
import pylab
import numpy
import mlpy
# 2 dimensional data
data = numpy.array([[1,1], [2,2.3], [3, 3.4]])
pylab.scatter(data[:,0], data[:,1], label="orig_data")
pca = mlpy.PCA()
pca.learn(data)
# plot principal components
coef = pca.coeff() # column1=pc1 , column2=pc2
pylab.plot([0,coef[0,0]] ,[0, coef[0,1]], '-r', label="first PC" )
pylab.plot([0,coef[1,0]] ,[0, coef[1,1]], '-b', label="second PC")
# dimensionality reduction / reconstruction
z = pca.transform(data,k=1) # reduce data to the first principal component
rec = pca.transform_inv(z) # reconstruct the original data from the first principal component
# plot reconstructed data
pylab.plot(rec[:,0], rec[:,1], '+r', label="reconstructed data")
pylab.legend()
===== Mixture Models =====
Try [[PyMix]]
====== Networking ======
===== Simple HTTP Download =====
import urllib
url = urllib.urlopen("http://10.101.21.115:8080/display/servlet/graph?chart=avg&day=20061018§ionid=0303-0304")
f = file("out.png", "w")
f.write(url.read())
f.close()
===== Very simple HTTP Servers =====
With [[http://docs.python.org/2/library/simplehttpserver.html|SimpleHTTPServer]] contents of the current directory can be served via HTTP.
# serve current directory @ port 8000
python -m SimpleHTTPServer
The port can be supplied as first argument. For ports < 1024 root privileges are required (which is not recommended due to security problems on StackOverflow)
sudo python -m SimpleHTTPServer 80
===== Simple HTTP Servers =====
from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
from urlparse import urlparse, parse_qs
class MyServer(BaseHTTPRequestHandler):
def do_GET(self):
params = parse_qs(urlparse(self.path).query) # get request parameters
self.send_message("hello " + params['name'][0])
def do_POST(self):
content_len = int(self.headers.getheader('content-length'))
post_body = self.rfile.read(content_len)
self.send_message("Ok. Got Message " + post_body)
def send_message(self, message):
self.send_response(200, 'OK')
self.send_header('Content-type', 'text/html')
self.end_headers()
self.wfile.write(message)
@staticmethod
def serve_forever(port):
HTTPServer(('', port), MyServer).serve_forever()
if __name__ == "__main__":
MyServer.serve_forever(8080)
see http://www.doughellmann.com/PyMOTW/BaseHTTPServer/index.html for a lot of interesting remarks
===== Socket Server =====
import SocketServer
class EchoRequestHandler(SocketServer.BaseRequestHandler ):
def setup(self):
print self.client_address, 'connected!'
self.request.send('hi ' + str(self.client_address) + '\n')
def handle(self):
data = 'dummy'
while data:
data = self.request.recv(1024)
self.request.send(data)
if data.strip() == 'bye':
return
def finish(self):
print self.client_address, 'disconnected!'
self.request.send('bye ' + str(self.client_address) + '\n')
#server host is a tuple ('host', port)
server = SocketServer.ThreadingTCPServer(('', 50008), EchoRequestHandler)
server.serve_forever()
===== Using FTP =====
==== FTP Download ====
from ftplib import FTP
ftp = FTP()
ftp.connect("127.0.0.1", port=21)
ftp.login("username", "password")
ftp.cwd('dir/subdir/subdir')
local_file = open("local_file.xml", 'wb')
cmd = 'RETR ' + "file.xml"
ftp.retrbinary(cmd, local_file.write)
f.close()
ftp.quit()
==== FTP Upload ====
from ftplib import FTP
ftp = FTP("ftp.theftp.org")
ftp.login("username", "password")
ftp.cwd('subdirectory/subdirectory')
f = open(filepath, 'rb')
cmd = 'STOR ' + fileName
ftp.storbinary(cmd, f)
f.close()
ftp.quit()
====== Numpy ======
After having to reduce the memory footprint for one of my scripts I realized how **much more efficient** numpy arrays are compared to the default python lists. If you have large arrays of data you should strongly consider using numpy.
===== External Documentation =====
* http://docs.scipy.org/doc/
* http://www.scipy.org/Tentative_NumPy_Tutorial
===== Creating Arrays =====
import numpy
arr = numpy.zeros((96,4)) # create two dimensional array with values initialized to 0
===== Additional views on arrays =====
Use views on arrays to add new functionality, reshape arrays, etc. - all without copying the actual data
import numpy as np
x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) # access via z["a"]
z = x.view(np.recarray)
print z.a # recarrays allow access with attributes
===== Read a CSV File into a numpy ndarray =====
Easy way with [[http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html|numpy.genfromtxt]]
import numpy
data= numpy.genfromtxt("data.csv", delimiter=";", names=True)
#accessing data
column_x = data["x"] # access a column
row1 = data[1] # access a row
row1_x = data[1]["x"] # access column "x" of row 1
Another, more verbose, method ( TODO: remove )
import numpy
lines = file("cv_in.txt").readlines()
rows = len(lines)
cols = len(lines[0].strip().split("\t"))
values = numpy.zeros( (rows,cols))
count = 0
for line in lines:
values[count,] = [ float(x) for x in line.strip().split("\t") ]
count += 1
====== Optimisation ======
===== Cython =====
A nice introduction can be found here http://www.perrygeo.net/wordpress/?p=116
===== Pandas =====
[[Pandas]]
====== Parallel Computing ======
===== Threading vs. Multiprocessing =====
Python < 2.6 supports threads trough the "threading" module. An important note however: CPython currently has something called a //Global Interpreter Lock// (http://en.wikipedia.org/wiki/Global_Interpreter_Lock). In short this means that only one thread is allowed to use the Python interpreter. As a consequence this highly limits the concurrency of a single process with multiple threads.
What this means is that in Python < 2.6 you will **not gain performance** from running a python numbercrunching application with multiple threads! You will only gain performance if your threads are strongly IO bound!
Starting with Python 2.6 the multiprocessing module is made available. This module circumvents the GIL by using subprocesses and is the preferred option to parallelize python calculations (see http://docs.python.org/library/multiprocessing.html )
===== Multiprocessing =====
If you want true parallelism (without GIL restrictions) use the multiprocessing library. Here is a simple example using queues and Worker Processes:
import multiprocessing as mp
import Queue # for catching Queue.Empty
import random
import time
class Result(object):
def __init__(self, v):
self.v = v
def value(self):
return self.v
def iterate(queue):
"""helper to iterate over a queue until it is empty"""
while True:
try:
yield queue.get_nowait()
except Queue.Empty:
break
class Worker(mp.Process):
def __init__(self, i, qin, qout):
super(Worker, self).__init__()
self.id = i
self.qin = qin
self.qout = qout
def run(self):
for data in iterate(self.qin):
print "worker %d has data: %d" % (self.id, data)
time.sleep(random.randint(0, 1))
self.qout.put(Result(data * 2))
print "Ending Worker %d" % self.id
mgr = mp.Manager() # creating Queues without a Manager will lead to strange behaviour
q_in = mgr.Queue()
q_out = mgr.Queue()
# create data
for i in range(250):
q_in.put(i)
# create workers
workers = []
for i in range(20):
w = Worker(i, q_in, q_out)
workers.append(w)
w.start()
# wait for the workers to finish
for w in workers:
w.join()
# process results
while not q_out.empty():
result = q_out.get()
print result.value()
===== Threading =====
==== Create a Worker Class ====
import threading
class Worker(threading.Thread):
def run(self):
doSomeWork()
for i in range(3):
worker = Worker()
worker.start()
==== Execute a method as a thread ====
def worker_method():
doSomething()
t = Thread(target=worker_method)
t.start()
===== Queues =====
Queues can be used to pass data around in a thread-safe manner. See http://www.python.org/doc/2.5.2/lib/QueueObjects.html for details.
from threading import Thread
from Queue import Queue
def worker():
while True:
item = q.get() # this call BLOCKS if the queue is empty.
# Use get_nowait() if you would rather like an Exception.
do_work(item)
q.task_done() # THIS is important!
q = Queue()
for i in range(num_worker_threads):
t = Thread(target=worker)
t.setDaemon(True)
t.start()
for item in items_to_process:
q.put(item)
q.join() # blocks until all itmes are processed
**Note//' the '//q.task_done()** call in the worker. This tells the queue that processing one item has finished. If your workers for some reason dont call this ( eg. because of an Exception) then the call q.join() will NEVER UNBLOCK!
===== External Documentation =====
* http://docs.python.org/library/threading.html
====== Pylab recipes ======
[[PylabRecipes]]
====== Regular Expressions ======
===== Find out if something does (not) match =====
import re
pattern = re.compile("^\d")
match = pattern.match("my text")
if match:
print "line starts with number"
else:
print "line does not start with number"
===== Split a String =====
To split a string by a simple delimiter just use string.split(). For a more complex splitting operation:
import re
s = "a 1 and 2 and 3 and 4"
a = re.split("\d", s) # every number is a delimiter
===== Extract Data Using Subgroups =====
==== Single Match ====
Either use a compiled pattern:
import re
p = re.compile("x=(\d+?).*?y=(\d+?)")
match = p.search("blah x=3 y=4 and ")
(x,y) = match.groups()
print x,y
Or use the package function:
import re
matches= re.search("x=(\d+?)[ ]*?y=(\d+?)[ ]","blah x=3 y=4 and ")
(x,y) = matches.groups()
print x,y
==== Multiple Matches ====
Either use a **compiled pattern**:
import re
p = re.compile("x=(\d+?).*?y=(\d+?)")
mItr = p.finditer("x=3 y=4 and then x=5, y=7 and x=8, y=9") # return iterator
for m in mItr :
(x,y) = m.groups()
#OR: matches = p.findall("x=3 y=4 and then x=5, y=7 and x=8, y=9") # return array of tuples
Or use the **package function**
import re
matches = re.findall("x=(\d+?).*?y=(\d+?)","x=3 y=4 and then x=5, y=7 and x=8, y=9") # or re.finditer
==== Selecting Groups From a Match ====
The .groups() method can optionally take a sequence that indicates which groups should be returned (group names or indices). eg.
(x,y) = match.groups([1,4]) #only get first and fourth group from the match
====== Sending Mail ======
import smtplib
msg="Subject: Subject \n\nBlablablablabla"
smtp = smtplib.SMTP("localhost")
smtp.sendmail("root", reciever, msg)
smtp.quit()
====== Scipy ======
===== Installing =====
sudo aptitude install python-scipy
===== Interpolation =====
Interpolation is the process of using a set of data values for a function to determine the missing values of that function. Scipy provides a lot of functionality for this. See [[[http://www.cs.mun.ca/~rod/2500/notes/interpolation/interpolation.html|here]]]
Simple Example:
import numpy
import scipy.interpolate
orig_data = numpy.array([1,2,3,0,5,0,5,0,7]) # data to smooth
# find x,y positions between which to interpolate
x_data = [i for i in range(len(orig_data)) if orig_data[i] > 0] # indices where orig_data is valid
y_data = orig_data[x_data] # the valid data in orig_data at the corresponding indices
spline = scipy.interpolate.splrep(x_data,y_data,s=200) # calculate the spline
smoothed_data = scipy.interpolate.splev(range(len(orig_data-1)), spline) # calculate a complete series
# optional: plot data
import pylab
pylab.plot(orig_data)
pylab.plot(smoothed_data)
===== Regression =====
==== Linear Regression ====
import numpy
import scipy
# data to fit
x = numpy.arange(0,9)
y = [19, 20, 20.5, 21.5, 22, 23, 23, 25.5, 24]
# calculate regression parameters ( y_fitted = a + bx )
[a,b] = scipy.polyfit(x,y,1)
# calculate fit
y_fitted = scipy.polyval([a,b],x)
Ordinary Linear Least Squares Fit using mlpy
import numpy
import pylab
import mlpy
# data to learn
x = numpy.random.normal(1, 5, 50)
x = x.reshape(-1,1) # need to transform x, the features of each datapoint must be in one row
y = numpy.random.normal(2,2, 50)
# Ordinary least squares fit
ols = mlpy.OLS()
ols.learn(x,y)
# predict data using the learned regression
x1 = numpy.arange(-20,20,0.5).reshape(-1,1) # features of one input-point must be in a row
y1 = ols.pred(x1)
# plot
pylab.scatter(x,y)
pylab.plot(x1,y1)
===== Smoothing Data =====
A smoothing function that does exactly the same as the Matlab function "smooth" (from http://www.scipy.org/Cookbook/SignalSmooth )
def smooth(x,window_len=5,window='flat'):
if x.ndim != 1:
raise ValueError, "smooth only accepts 1 dimension arrays."
if x.size < window_len:
raise ValueError, "Input vector needs to be bigger than window size."
if window_len<3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError, "Window has to be one of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"
s=numpy.r_[2*x[0]-x[window_len-1::-1],x,2*x[-1]-x[-1:-window_len:-1]]
if window == 'flat': #moving average
w=numpy.ones(window_len,'d')
else:
w=eval('numpy.'+window+'(window_len)')
y=numpy.convolve(w/w.sum(),s,mode='same')
return y[window_len:-window_len+1]
===== Spline Polynoms =====
import numpy
import scipy.signal
y = numpy.array([41.621207814814809, 42.328298238095236, 45.881729878787887, 43.800834224999996])
y_smoothed = scipy.signal.cspline1d(y)
===== t-test =====
from scipy import stats
import numpy
import statistics
# http://www.biostathandbook.com/onesamplettest.html
data = [120.6, 116.4,117.2,118.1,114.1,116.9,113.3,121.1,116.9,117.0]
m = sum(data)/len(data)
null_hypothesis = 120
t_value, p_value = stats.ttest_1samp(data, null_hypothesis)
print(statistics.stdev(data))
print(numpy.std(data, ddof=1))
print(t_value, p_value)
====== Statistical Functions ======
===== Pylab =====
Pylab provides a number of simple statistical functions:
* pylab.mean
* pyalb.median
* pylab.var
===== Python-Statlib =====
The google project [[[http://code.google.com/p/python-statlib/|Python-Statlib]] is the most complete statistical library for python I have found so far.
==== Installation ====
Unfortunately there is currently no ubuntu package, so you will have to download the latest .tar.gz from http://code.google.com/p/python-statlib/downloads/list and install it by extracting it and running:
sudo python setup.py install
==== Example ====
from statlib import stats
mean = stats.mean([1,2,3,4,5])
A complete list of the supported statistical functions can be found at http://code.google.com/p/python-statlib/wiki/StatsDoc
==== Running Median ====
Normally it is advisable to use pylab.median .... but if you have so many values that they don't fit into memory anymore, there is a trick that can give you a rough estimate for the median:
def running_median(v,step_size=0.01):
""" Estimate median from v. Warning: this will be inaccurate unless there are MANY values in v! """
median = v[0]
for i in range(len(v)):
inc = step_size if v[i] > median else -step_size
median += inc
return median
# test accurracy of the approach
errors=[]
for i in range(500):
print i
v = numpy.random.uniform(0,100,size=50000)
errors.append(running_median(v) - pylab.median(v))
pylab.hist(errors,bins=50)
(found in comments [[http://programmingpraxis.com/2012/05/29/streaming-median/|here]] - damn I hate paywalls. I'd love to read the piglet tracking paper!)
====== Testing ======
===== pytest =====
https://docs.pytest.org/en/latest/example/
https://docs.pytest.org/en/latest/goodpractices.html#choosing-a-test-layout-import-rules
===== unittest =====
* Python 2 https://docs.python.org/2.7/library/unittest.html
* python 3 https://docs.python.org/3.4/library/unittest.html
* http://agiletesting.blogspot.com/2005/01/python-unit-testing-part-1-unittest.html the nutshell
==== Basics ====
import unittest
class CalculationTest(unittest.TestCase):
# before each test
def setUp(self):
self.x = 23
# tests = methods whose name starts with 'test', executed in order of their function name
def testGetArithAvg(self):
self.assertEquals(23, self.x)
# execute all tests
if __name__ == '__main__':
unittest.main()
==== classwide Setup and teardown ====
There are two class methods that are called before/after tests in an individual class run.
**setUpClass** and **tearDownClass** are called with the class as the only argument and must be decorated as a classmethod():
import unittest
class MyTest(unittest.case.TestCase):
@classmethod
def setUpClass(cls):
cls.msg = "I am upset!"
def test_hello(self):
print self.msg
@classmethod
def tearDownClass(cls):
cls.msg = None
if __name__ == '__main__':
unittest.main()
==== Test for exceptions ====
Since [[http://docs.python.org/2/library/unittest.html#unittest.TestCase.assertRaises|python 2.7]] this is best done by using the context manager returned by unittest.assertRaises()
with self.assertRaises(SomeException):
test_something_that_raises_exception()
For older python versions see [[http://stackoverflow.com/questions/6103825/how-to-properly-use-unit-testings-assertraises-with-nonetype-objects|this SO question]].
====== Web Services ======
There are several python frameworks available.
===== ZSI =====
ZSI is a framework which supports webservice servers and clients. And has support for wsdl2python as well as dynamic webservice calls via a ServiceProxy. See http://pywebsvcs.sourceforge.net/holger.pdf
sudo aptitude install python-zsi
===== Accessing a Webservice with ZSI =====
import sys
from ZSI.ServiceProxy import ServiceProxy
wsdlUrl='http://www2.meteomedia.at/wetter_verkehr/weather_data.php?wsdl'
service = ServiceProxy(wsdlUrl, tracefile=sys.stdout)
service.getLastCalculationTime()
==== ServiceProxy and caching ====
A very important note: ZSI ServiceProxy creates a cache where it puts all the python classes generated from a WSDL. AND DOES NOT REFRESH THAT CACHE for you. So if you are wondering why changes made to a WSDL you are fetching are not reflected in the Python classes you try to use in your client, have a look at ~/.zsi_service_proxy_dir (ZSI 2.1) or ./.service_proxy_dir (ZSI 2.0) and clean up!
It might also be useful to **explicitly** control which directory is used by:
service = ServiceProxy(wsdlUrl,cachedir='/tmp/zsi_test/', tracefile=sys.stdout)
==== Datetimes ====
Datetimes are tricky (see http://pywebsvcs.sourceforge.net/zsi.html#SECTION007600000000000000000 and http://pywebsvcs.sourceforge.net/cookbook.pdf for details): basically ZSI does **not** expect a datetime to be a string in the standard xs:datetime format, or a python datetime. Instead it expects a python timetuple in UTC, which means timezones are not supported.
dt = datetime.datetime.now().timetuple() # valid parameter for a webservice request
===== Soappy =====
**Soappy is DEPRECATED and should no longer be used**
sudo aptitude install python-soappy
See http://www.ebi.ac.uk/Tools/webservices/tutorials/python for tutorials
===== Accessing a Webservice with Soappy =====
from SOAPpy import WSDL
wsdlUrl = 'http://1.2.3.4:8000/dynamicroutermodule?wsdl'
service = WSDL.Proxy(wsdlUrl)
request={}
request[[fromRoadId]]=10400000586647
request[[fromRoadDirection]]=1
request[[toRoadId]]=10400002879088
request[[toRoadDirection]]=1
request[[routingType]]=0
route = service.getRoute(arg0=request)
===== Writing a report file =====
==== docx ====
==== rtf ====
pyth comes with conversion tools but offers no image support.
pyrtf-ng
====== XML Processing ======
===== ElementTree =====
import xml.etree.ElementTree
#
# Parse
#
# parse xml from file
root = xml.etree.ElementTree.parse(filename).getroot() # parse() yields an ElementTree object so we need to explicitly call getroot()
# parse xml from string
root = xml.etree.ElementTree.fromstring(xml_str) # fromstring() directly yields the root element
#
# Search
#
# find a tag via xpath
gisroute = root.find("GisRes/GisRoute")
# find multiple tags via xpath
connections = root.findall("ConnectionList/Connection")
#
# Access
#
# access attributes
route_id = gisroute.attrib["id"]
# access text
txt = gisroute.text
===== DOM =====
==== Parsing a Document ====
import xml.dom.minidom
from xml.dom.minidom import Node
doc = xml.dom.minidom.parse("maps.xml")
for node in doc.getElementsByTagName("Placemark"):
#do something to node
==== Getting an Attribute ====
//Extract the "muh" from
att = node.getAttribute("att")
==== Getting text from content of node ====
//Extract the "muh" from
for node in parent.childNodes:
if node.nodeType == Node.TEXT_NODE:
print node.data
==== Creating a XML doc ====
from xml.dom.minidom import Document
# Create the minidom document
doc = Document()
# Create the base element
wml = doc.createElement("wml")
doc.appendChild(wml)
# Create the main element
maincard = doc.createElement("card")
maincard.setAttribute("id", "main")
wml.appendChild(maincard)
# Create a element
paragraph1 = doc.createElement("p")
maincard.appendChild(paragraph1)
# Give the
elemenet some text
ptext = doc.createTextNode("This is a test!")
paragraph1.appendChild(ptext)
# save
out=file('out.xml','w')
doc.writexml(out)
out.close()
(example from http://www.postneo.com/projects/pyxml/)
===== SAX =====
//see http://wiki.python.org/moin/Sax //
====== Design Patterns ======
===== Singleton =====
class Singleton:
__shared_state = {}
def __init__(self):
self.__dict__ = self.__shared_state
s1 = Singleton()
s1.x = 1
s2 = Singleton()
s2.x # 1
===== Iterator =====
def datetimeIterator(from_date=datetime.now(), to_date=None, delta=timedelta(days=1) ):
while to_date is None or from_date <= to_date:
yield from_date from_date = from_date + delta
return
for d in datetimeIterator(datetime.strptime("20090101","%Y%m%d"),datetime.strptime("20090610","%Y%m%d")):
print datetime.strptime("20090104","%Y%m%d")==d