3 rd year Bachelors
V1.0
dd 04-09-2013
Hour 1

– txt
– csv
– FITS > pyFITS, astropy.io.fits

>>> s = 'Hello, world.'
>>> str(s)
'Hello, world.'
>>> repr(s)
"'Hello, world.'"
>>> str(1.0/7.0)
'0.142857142857'
>>> repr(1.0/7.0)
'0.14285714285714285'
>>> x = 10 * 3.25
>>> y = 200 * 200
>>> s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'
>>> print s
The value of x is 32.5, and y is 40000...
>>> # The repr() of a string adds string quotes and backslashes:
... hello = 'hello, world\n'
>>> hellos = repr(hello)
>>> print hellos
'hello, world\n'
>>> # The argument to repr() may be any Python object:
... repr((x, y, ('spam', 'eggs')))
"(32.5, 40000, ('spam', 'eggs'))"

>>> import math
>>> print 'The value of PI is approximately %5.3f.' % math.pi
The value of PI is approximately 3.142.
>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}
>>> for name, phone in table.items():
... print '{0:10} ==> {1:10d}'.format(name, phone)
...
Jack ==> 4098
Dcab ==> 7678
Sjoerd ==> 4127

A conversion specifier contains two or more characters and has the following components, which must occur in this order:
• The "%" character, which marks the start of the specifier.
• Mapping key (optional), consisting of a parenthesised sequence of characters (for example,
(somename)).
• Conversion flags (optional), which affect the result of some conversion types.
• Minimum field width (optional). If specified as an "*" (asterisk), the actual width is read from the next element of the tuple in values , and the object to convert comes after the minimum field width and optional precision.
• Precision (optional), given as a "." (dot) followed by the precision. If specified as "*" (an asterisk), the actual width is read from the next element of the tuple in values , and the value to convert comes after the precision.
• Length modifier (optional).
• Conversion type.
>>> print '%(language)s has %(#)03d quote types.' % \
{'language': "Python", "#": 2}
Python has 002 quote types.

c r s
%
E f
F g i o
Conversion d u x
X e
G
Meaning
Signed integer decimal.
Signed integer decimal.
Unsigned octal.
Unsigned decimal.
Unsigned hexadecimal (lowercase).
Unsigned hexadecimal (uppercase).
Floating point exponential format (lowercase).
Floating point exponential format (uppercase).
Floating point decimal format.
Floating point decimal format.
Same as "e" if exponent is greater than -4 or less than precision, "f" otherwise.
Same as "E" if exponent is greater than -4 or less than precision, "F" otherwise.
Single character (accepts integer or single character string).
String (converts any python object using repr()).
String (converts any python object using str()).
No argument is converted, results in a "%" character in the result.

>>> print “Python is great,”, ”isn’t it?”
>>> str = raw_input( “Enter your input: ”)
>>> print “Received input is: “,str
Enter your input: Hello Python
Received input is: Hello Python
>>> str = input("Enter your input: ");
>>> print "Received input is: ", str
Enter your input: [x*5 for x in range(2,10,2)]
Received input is: [10, 20, 30, 40]
If the readline modules was loaded the raw_input() will use it to provide elaborate line editing and history features.

>>> fname = ‘myfile.dat’
>>> f = file(fname)
>>> lines = f.readlines()
>>> f.close()
>>> f = file(fname)
>>> firstline = f.readline()
>>> secondline = f.readline()
>>> f = file(fname)
>>> for l in f:
... print l.split()[1]
>>> f.close()
>>> outfname = ‘myoutput’
>>> outf = file(outfname, ‘w’) # second argument denotes writable
>>> outf.write(‘My very own file\n’)
>>> outf.close()

>>> f = open("test.txt")
>>> # Read everything into single string:
>>> content = f.read()
>>> len(content)
>>> print content
>>> f.read() # At End Of File
>>> f.close()
>>> # f.read(20) reads (at most) 20 bytes
Using with block:
>>> with open(’test.txt’, ’r’) as f:
... content = f.read()
>>> f.closed
CSV file:
>>> import csv
>>> ifile = open(’photoz.csv’, "r")
>>> reader = csv.reader(ifile)
>>> for row in reader:
... print row,
>>> ifile.close()

>>> from numpy import *
>>> data = loadtxt("myfile.txt") # myfile.txt contains 4 columns of numbers
>>> t,z = data[:,0], data[:,3] # data is a 2D numpy array, t is 1 st col, z is 4 th col
>>> t,x,y,z = loadtxt("myfile.txt", unpack=True) # to automatically unpack all columns
>>> t,z = loadtxt("myfile.txt", usecols = (0,3), unpack=True) # to select just a few columns
>>> data = loadtxt("myfile.txt", skiprows = 7) # to skip 7 rows from top of file
>>> data = loadtxt("myfile.txt", comments = '!') # use '!' as comment char instead of '#'
>>> data = loadtxt("myfile.txt", delimiter=';') # use ';' as column separator instead of whitespace
>>> data = loadtxt("myfile.txt", dtype = int) # file contains integers instead of floats
>>> from numpy import *
>>> savetxt("myfile.txt", data) # data is 2D array
>>> savetxt("myfile.txt", x) # if x is 1D array then get 1 column in file.
>>> savetxt("myfile.txt", (x,y)) # x,y are 1D arrays. 2 rows in file.
>>> savetxt("myfile.txt", transpose((x,y))) # x,y are 1D arrays. 2 columns in file.
>>> savetxt("myfile.txt", transpose((x,y)), fmt='%6.3f') # use new format instead of '%.18e'
>>> savetxt("myfile.txt", data, delimiter = ';') # use ';' to separate columns instead of space

>>> sigma = 6.76/2.354
>>> print(‘sigma is %5.3f metres’%sigma) sigma is 2.872 metres
>>> d = {‘bob’: 1.87, ‘fred’: 1.768}
>>> for name, height in d.items():
... print(‘%s is %.2f metres tall’%(name.capitalize(), height))
...
Bob is 1.87 metres tall
Fred is 1.77 metres tall
>>> nsweets = range(100)
>>> calories = [i * 2.345 for i in nsweets]
>>> fout = file(‘sweetinfo.txt’, ‘w’)
>>> for i in range(nsweets):
... fout.write(‘%5i %8.3f\n’%(nsweets[i], calories[i]))
...
>>> fout.close()

– csv.reader
– csv.writer
– csv.register_dialect
– csv.unregister_dialect
– csv.get_dialect
– csv.list_dialects
– csv.field_size_limit

•
Reading CSV files import csv # imports the csv module f = open('data1.csv', 'rb') # opens the csv file try: reader = csv.reader(f) # creates the reader object for row in reader: # iterates the rows of the file in orders finally: print row # prints each row f.close() # closing
• Writing CSV files import csv ifile = open('test.csv', "rb") reader = csv.reader(ifile) ofile = open('ttest.csv', "wb") writer = csv.writer(ofile, delimiter='\t', quotechar='"', quoting=csv.QUOTE_ALL) for row in reader: writer.writerow(row) ifile.close() ofile.close()

– can transform a complex object into a byte stream
– can transform the byte stream into an object with the same internal structure
– most obvious thing to do with these byte streams is to write them onto a file
– also conceivable to send them across a network or store them in a database
– None, True, and False
– integers, long integers, floating point numbers, complex numbers
– normal and Unicode strings
– tuples, lists, sets, and dictionaries containing only picklable objects
– functions defined at the top level of a module
– built-in functions defined at the top level of a module
– classes that are defined at the top level of a module
– instances of such classes whose __dict__ or the result of calling __getstate__() is picklable (see section The pickle protocol for details).

import pickle data1 = {'a': [1, 2.0, 3, 4+6j],
'b': ('string', u'Unicode string'),
'c': None} selfref_list = [1, 2, 3] selfref_list.append(selfref_list) output = open('data.pkl', 'wb')
# Pickle dictionary using protocol 0.
pickle.dump(data1, output)
# Pickle the list using the highest protocol available.
pickle.dump(selfref_list, output, -1) output.close()

import pprint, pickle data1 = pickle.load(pkl_file) pprint.pprint(data1) data2 = pickle.load(pkl_file) pprint.pprint(data2) pkl_file.close()
pkl_file = open('data.pkl', 'rb')
(dp0
S'a' p1
(lp2
I1 aF2.0
aI3 ac__builtin__ complex p3
(F4.0
F6.0
tp4
Rp5 asS'c' p6
NsS'b' p7
(S'string' p8
VUnicode string p9 tp10 s.
� ]q

– client – server model
– exchanging code & data for remote processing def dpu_packit(*args): return pickle.dumps(args) def dpu_unpackit(data): return pickle.loads(data)
# Sender side: def submitremotejobs(self, key, zip=None, jobs=[], env=None): if not len(jobs): return False return self.senddata(key, dpu_packit((zip, env), jobs))
# Receiver side: data = self.get_data()
((code, env), jobdictlist) = dpu_unpackit(data) make_code_file(key, code)

• URLS can be used for reading
>>> import urllib2
>>> url = 'http://python4astronomers.github.com/_downloads/data.txt'
>>> response = urllib2.urlopen(url)
>>> data = response.read()
>>> print data
RAJ DEJ Jmag e_Jmag
2000 (deg) 2000 (deg) 2MASS (mag) (mag)
---------- ---------- ----------------- ------ ------
010.684737 +41.269035 00424433+4116085 9.453 0.052
010.683469 +41.268585 00424403+4116069 9.321 0.022
010.685657 +41.269550 00424455+4116103 10.773 0.069
010.686026 +41.269226 00424464+4116092 9.299 0.063
010.683465 +41.269676 00424403+4116108 11.507 0.056
010.686015 +41.269630 00424464+4116106 9.399 0.045
010.685270 +41.267124 00424446+4116016 12.070 0.035

• URLS sometimes need input data. Such as POST data for a form import urllib import urllib2 url = 'http://www.someserver.com/cgi-bin/register.cgi' values = {'name' : 'Michael Foord',
'location' : 'Northampton',
'language' : 'Python' } data = urllib.urlencode(values) req = urllib2.Request(url, data) response = urllib2.urlopen(req) the_page = response.read()

• And for GET type of parameter passing: import urllib import urllib2>>> import urllib2
>>> import urllib
>>> data = {}
>>> data['name'] = 'Somebody Here'
>>> data['location'] = 'Northampton'
>>> data['language'] = 'Python'
>>> url_values = urllib.urlencode(data)
>>> print url_values # The order may differ. name=Somebody+Here&language=Python&location=Northampton
>>> url = 'http://www.example.com/example.cgi'
>>> full_url = url + '?' + url_values
>>> handler = urllib2.urlopen(full_url)
Note that the full URL is created by adding a ? to the URL, followed by the encoded values.

End