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And by everything they mean _everything_. For example it is legal to do the following
#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.
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:
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
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 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()
(from 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
l=[3,2,15,3,2,1,4] lSorted = sorted(l)
Default sorting order is ascending
l = [3,2,15,3,2,1,4] lReverseSorted = sorted(l, reverse=True)
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)
tmp = [[[1,2,3],[3],[6,7]]] sorted(tmp, lambda x,y: len(x)-len(y))
import operator items = sorted(my_dict.items(), key=operator.itemgetter(1)) # sort by value, itemgetter(0) to sort by key
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)
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]
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]
l = [[[1,2,3],[4,5,6], [7], [8,9]]] [item for sublist in l for item in sublist]
The docs of 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([(i, chr(65+i)) for i in range(4)])
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 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 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 diveintopython.org for more.
g = lambda x: x*2 # g(3) will return 6
Those two keywords act boolean but they return one of the values they compare. Evaluation is from left to right. 0, //, [], (), {}, and None are false in a boolean context; everything else is true. As can be expected, for and: if every value is true, the last true one is returned. If every value is false, the first false one is returned. For or it is the other way round.
In Python 2.5 there is a built in ternary operator (if?then:else ):
x = 12 if (y>0) else -12
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 diveintopython.org] for more.
import qgisfleettools as q q.doStuff() reload(q)
Use 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
* 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() )
i = 4 "%d" % (i,) # --> "4" "%4d" % (i,) # --> " 4" "%04d" % (i,) # --> "0004" j = 1.3 "%.2f" % (j) # --> "1.30" ''
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
"muh".encode('rot13')
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 ...
attrs helps with
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
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
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()
import sys print sys.argv[1]
I have found several builtin modules to deal with commandline parameters. The most flexible and object oriented seems to be OptionParser.
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
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()
There appear to be several ways to read in config files. This example uses the ConfigParser class.
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.
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")
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)
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.
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"]
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 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
sudo apt-get install python-psycopg2
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"]
In order to use server-side cursors with PostgreSQL/psycopg2 one just needs to use psycopg2's named cursor and set an adequate itersize
cursor = conn.cursor(name='a_cursor') cursor.itersize = 10000 for row in cursor: pass
For details see e.g. psycopg2's server-side doc on cursors and the PostgreSQL cursor doc
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)
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
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.
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()
sudo apt-get install python-pysqlite2 sqlite3
Note: python-pysqlite2 creates sqlite3 databases. SQLite2 and SQLite3 are not compatible!
from pysqlite2 import dbapi2 as sqlite connection = sqlite.connect("test.db") # or use :memory: to create an in-memory database cursor = connection.cursor()
cursor.execute("attach '/var/fleet/output/trips.db' as trips")
cursor.execute("SELECT * from names") print cursor.fetchall() # # or # for row in cursor: print row[0] connection.close()
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)
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]
According to 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')" )
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:
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
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()
A good introduction to mysql-python can be found at http://mysql-python.sourceforge.net/MySQLdb.html#mysqldb
sudo apt-get install python-mysqldb
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()
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()
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 ''
There are (at least) two separate ways to deal with dates:
import datetime current_time = datetime.datetime.now()
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 (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)
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)
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)
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)
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
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
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)
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")
(from 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()
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
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”
One way to handle nasty Umlauts: (check 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
(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()
import fileinput for line in fileinput.input(onefilename, inplace=1): print line.replace(old,new)
import os os.path.exists("/path/to/some/where")
import os path,filename = os.path.split(absolute_path)
import os if not os.path.isdir(dir): os.makedirs(dir) # creates all non-existent directories in the path of dir
(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 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")
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
import tempfile tempfile.mkstemp() # create temporary file tempfile.mkdtemp(".tmp", "backup_") #create a temp directory with prefix and suffix. eg: /tmp/backup_rt5JWA.tmp
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.
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)
# 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")
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:
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
From here:
count=24 n=286476 print "".join([str((n >> y) & 1) for y in range(count-1, -1, -1)])
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)]
Interesting libraries
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')
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)
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()
Try PyMix
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()
With 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
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
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()
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()
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()
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.
import numpy arr = numpy.zeros((96,4)) # create two dimensional array with values initialized to 0
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
Easy way with 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
A nice introduction can be found here http://www.perrygeo.net/wordpress/?p=116
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 )
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()
import threading class Worker(threading.Thread): def run(self): doSomeWork() for i in range(3): worker = Worker() worker.start()
def worker_method(): doSomething() t = Thread(target=worker_method) t.start()
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!
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"
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
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
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<tuple> 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
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
import smtplib msg="Subject: Subject \n\nBlablablablabla" smtp = smtplib.SMTP("localhost") smtp.sendmail("root", reciever, msg) smtp.quit()
sudo aptitude install python-scipy
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 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)
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)
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]
import numpy import scipy.signal y = numpy.array([41.621207814814809, 42.328298238095236, 45.881729878787887, 43.800834224999996]) y_smoothed = scipy.signal.cspline1d(y)
Pylab provides a number of simple statistical functions:
The google project Python-Statlib is the most complete statistical library for python I have found so far.
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
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
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 here - damn I hate paywalls. I'd love to read the piglet tracking paper!)
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()
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()
Since 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 this SO question.
There are several python frameworks available.
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
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()
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 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 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
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)
pyth comes with conversion tools but offers no image support.
pyrtf-ng
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
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
Extract the “muh” from <node att=“muh” />
att = node.getAttribute("att")
Extract the “muh” from <parent>MUH</parent>
for node in parent.childNodes: if node.nodeType == Node.TEXT_NODE: print node.data
from xml.dom.minidom import Document # Create the minidom document doc = Document() # Create the <wml> base element wml = doc.createElement("wml") doc.appendChild(wml) # Create the main <card> element maincard = doc.createElement("card") maincard.setAttribute("id", "main") wml.appendChild(maincard) # Create a <p> element paragraph1 = doc.createElement("p") maincard.appendChild(paragraph1) # Give the <p> 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/)
class Singleton: __shared_state = {} def __init__(self): self.__dict__ = self.__shared_state s1 = Singleton() s1.x = 1 s2 = Singleton() s2.x # 1
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