Remote Debugging Download Eclipse Download PyDev over Eclipse Create a PyDev project, Update both pythons of server and client (eclipse) to same version. Link for centos python update Copy pydev plugin to server. The files are part of the eclipse plugin. To find the path on a mac $ find /Applications/eclipse/plugins name 'org.python.pydev_*' # e.g. /Applications/eclipse/plugins/org.python.pydev_2.8.2.2013090511 On server, find an appropriate location for the pydev files $ python c "import sys from pprint import pprint as pp pp(sys.path)" # e.g. /usr/local/lib/python2.7/distpackages ssh msnia@freeplay02.cise.ufl.edu mkdir pysrc scp r /Users/morteza/Downloads/transfer/apps/Eclipse.app/Contents/Eclipse/plugins/org.python.pydev_4.4.0.201510052309/pysrc/* msnia@freeplay02.cise.ufl.edu:pysrc ssh msnia@freeplay02.cise.ufl.edu 'sudo cp R pysrc/* /usr/local/lib/python2.7/distpackages/' ssh msnia@freeplay02.cise.ufl.edu rm r pysrc Configure path mapping. On the remote machine, edit /usr/local/lib/python2.7/distpackages/pydevd_file_utils.py to define the mapping between local and remote files. Here my remote is a vm and I've mounted the remote /home/vagrant at '/Users/brian/sandbox/vagrant/example.dev/nfsvagrant' PATHS_FROM_ECLIPSE_TO_PYTHON = [(r''/Users/morteza/zproject/workspaces/spokenlanguageunderstanding, r'/home/msnia/zproject/spokenlanguageunderstanding'), ] python memory $ python helloWorld.py OR $ chmod u+x helloWorld.py ./helloWorld.py Spacing at the start of the line to determine code blocks >>> myset = set(["mort", "b", "g", "g"]) >>> myset set(['b', 'g', 'mort']) String String + int : err String * int : StringStringStringStringString... String[int] : charat String[1] : last, 2 next to last String+String=String String[int?:int?] : substring start to before stop "assume"[4:6] me "assume"[4:] me "assume"[:2] as String.split() : gives words String.find(Stirng, index?) : index or 1 search after int index using """ triple quotes you can define strings that span across multiple lines str(int) The string equivalent of an integer a,x=x,a x and a swap valuesinput() actually evaluates the input as Python code. I suggest to never use it. raw_input() returns the verbatim string entered by the user. i=0 name1 = "initial value" while i <= 2 : name1 = raw_input("Enter your name: ") if name1 == "morteza" : print "Hi " + name1 elif name1 == "John" : print "Hi " + "Johhny" else : print "Hello world" i=i+1 for <var_name> in <list>: block function def hello(): print "hello" return 1234 print hello() # And here is the function being used def <name> (<params>): return s[1:], "second return" # tuple Tuples, Lists, and Dictionariesmonths = ('January','February','March','April','May','June',\ 'July','August','September','October','November',' December') months[0] # january lists are like tuples but their values can change cats = ['Tom', 'Snappy', 'Kitty', 'Jessie', 'Chester'] cats[2:4] # ['Kitty', 'Jessie'] cats.append(1) # ['Tom', 'Snappy', 'Kitty', 'Jessie', 'Chester', 1] del cats[1] # ['Tom', 'Kitty', 'Jessie', 'Chester', 1] LIST; point by reference p = ['a', 3, 'c', [3, 'k']] p[2:3] => [3, 'c'] p[3][1]='k' LIST1 + LIST2 => LIST3 len(LIST) LIST.index(value) # index or error <value> in LIST OR <value> not in LIST LIST.pop() remove and return last element dictionary phonebook = {'Andrew':8806336, 'Emily':6784346, 'Peter':7658344, \ 'Lewis':1122345} phonebook['Man'] = 1234567 # add new key/value del phonebook['Andrew'] if phonebook.has_key('Man'): print "exists" phonebook.keys() phonebook.values().sort() for value in list: print value id1,id2=myfunction(); $ multiple assignments None: is frequently used to represent the absence of a value, as when default arguments are not passed to a function. or just the value none. Class class Shape: Modulesdef __init__(self,x,y): self.x = x self.y = y print "New shape!" description = "This shape has not been described yet" def area(self): return self.x * self.y print "in class body" longrectangle = Shape(120,10) fatrectangle = Shape(130,120) class Square(Shape): def __init__(self,x): self.x = x self.y = x print "square" sq = Square(10)  in class body New shape! New shape! square in instance2 = instance1, instance2 is 'pointing' to instance1  there are two names given to the one class instance, and you can access the class instance via either name. A module is a python file that (generally) has only defenitions of variables, functions, and classes. As you see, a module looks pretty much like your normal python program.
IOopenfile = open('pathtofile', 'r') openfile.read() Objects / pickles
try/except try: a = input('Enter a number to subtract from > ') b = input('Enter the number to subtract > ') except NameError: print "\nYou cannot subtract a letter" continue except SyntaxError: print "\nPlease enter a number only." continue print a  b try: loop = input('Press 1 to try again > ') except (NameError,SyntaxError): loop = 0 Python Numpy TutorialThis tutorial was contributed by Justin Johnson. We will use the Python programming language for all assignments in this course. Python is a great generalpurpose programming language on its own, but with the help of a few popular libraries (numpy, scipy, matplotlib) it becomes a powerful environment for scientific computing. We expect that many of you will have some experience with Python and numpy; for the rest of you, this section will serve as a quick crash course both on the Python programming language and on the use of Python for scientific computing. Some of you may have previous knowledge in Matlab, in which case we also recommend the numpy for Matlab users page. Table of contents: PythonPython is a highlevel, dynamically typed multiparadigm programming language. Python code is often said to be almost like pseudocode, since it allows you to express very powerful ideas in very few lines of code while being very readable. As an example, here is an implementation of the classic quicksort algorithm in Python:
Python versionsThere are currently two different supported versions of Python, 2.7 and 3.4. Somewhat confusingly, Python 3.0 introduced many backwardsincompatible changes to the language, so code written for 2.7 may not work under 3.4 and vice versa. For this class all code will use Python 2.7. You can check your Python version at the command line by running
Basic data typesLike most languages, Python has a number of basic types including integers, floats, booleans, and strings. These data types behave in ways that are familiar from other programming languages. Numbers: Integers and floats work as you would expect from other languages:
Note that unlike many languages, Python does not have unary increment ( Python also has builtin types for long integers and complex numbers; you can find all of the details in the documentation. Booleans: Python implements all of the usual operators for Boolean logic,
but uses English words rather than symbols (
Strings: Python has great support for strings:
String objects have a bunch of useful methods; for example:
You can find a list of all string methods in the documentation. ContainersPython includes several builtin container types: lists, dictionaries, sets, and tuples. ListsA list is the Python equivalent of an array, but is resizeable and can contain elements of different types:
As usual, you can find all the gory details about lists in the documentation. Slicing: In addition to accessing list elements one at a time, Python provides concise syntax to access sublists; this is known as slicing:
We will see slicing again in the context of numpy arrays. Loops: You can loop over the elements of a list like this:
If you want access to the index of each element within the body of a loop,
use the builtin
List comprehensions: When programming, frequently we want to transform one type of data into another. As a simple example, consider the following code that computes square numbers:
You can make this code simpler using a list comprehension:
List comprehensions can also contain conditions:
DictionariesA dictionary stores (key, value) pairs, similar to a
You can find all you need to know about dictionaries in the documentation. Loops: It is easy to iterate over the keys in a dictionary:
If you want access to keys and their corresponding values, use the
Dictionary comprehensions: These are similar to list comprehensions, but allow you to easily construct dictionaries. For example:
SetsA set is an unordered collection of distinct elements. As a simple example, consider the following:
As usual, everything you want to know about sets can be found in the documentation. Loops: Iterating over a set has the same syntax as iterating over a list; however since sets are unordered, you cannot make assumptions about the order in which you visit the elements of the set:
Set comprehensions: Like lists and dictionaries, we can easily construct sets using set comprehensions:
TuplesA tuple is an (immutable) ordered list of values. A tuple is in many ways similar to a list; one of the most important differences is that tuples can be used as keys in dictionaries and as elements of sets, while lists cannot. Here is a trivial example:
The documentation has more information about tuples. FunctionsPython functions are defined using the
We will often define functions to take optional arguments, like this:
There is a lot more information about Python classes in the documentation. ClassesThe syntax for defining classes in Python is straightforward:
You can read a lot more about Python classes in the documentation. NumpyNumpy is the core library for scientific computing in Python. It provides a highperformance multidimensional array object, and tools for working with these arrays. If you are already familiar with MATLAB, you might find this tutorial useful to get started with Numpy. ArraysA numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension. We can initialize numpy arrays from nested Python lists, and access elements using square brackets:
Numpy also provides many functions to create arrays:
You can read about other methods of array creation in the documentation. Array indexingNumpy offers several ways to index into arrays. Slicing: Similar to Python lists, numpy arrays can be sliced. Since arrays may be multidimensional, you must specify a slice for each dimension of the array:
You can also mix integer indexing with slice indexing. However, doing so will yield an array of lower rank than the original array. Note that this is quite different from the way that MATLAB handles array slicing:
Integer array indexing: When you index into numpy arrays using slicing, the resulting array view will always be a subarray of the original array. In contrast, integer array indexing allows you to construct arbitrary arrays using the data from another array. Here is an example:
Boolean array indexing: Boolean array indexing lets you pick out arbitrary elements of an array. Frequently this type of indexing is used to select the elements of an array that satisfy some condition. Here is an example:
For brevity we have left out a lot of details about numpy array indexing; if you want to know more you should read the documentation. DatatypesEvery numpy array is a grid of elements of the same type. Numpy provides a large set of numeric datatypes that you can use to construct arrays. Numpy tries to guess a datatype when you create an array, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype. Here is an example:
You can read all about numpy datatypes in the documentation. Array mathBasic mathematical functions operate elementwise on arrays, and are available both as operator overloads and as functions in the numpy module:
Note that unlike MATLAB,
Numpy provides many useful functions for performing computations on
arrays; one of the most useful is
You can find the full list of mathematical functions provided by numpy in the documentation. Apart from computing mathematical functions using arrays, we frequently
need to reshape or otherwise manipulate data in arrays. The simplest example
of this type of operation is transposing a matrix; to transpose a matrix,
simply use the
Numpy provides many more functions for manipulating arrays; you can see the full list in the documentation. BroadcastingBroadcasting is a powerful mechanism that allows numpy to work with arrays of different shapes when performing arithmetic operations. Frequently we have a smaller array and a larger array, and we want to use the smaller array multiple times to perform some operation on the larger array. For example, suppose that we want to add a constant vector to each row of a matrix. We could do it like this:
This works; however when the matrix
Numpy broadcasting allows us to perform this computation without actually
creating multiple copies of
The line Broadcasting two arrays together follows these rules:
If this explanation does not make sense, try reading the explanation from the documentation or this explanation. Functions that support broadcasting are known as universal functions. You can find the list of all universal functions in the documentation. Here are some applications of broadcasting:
Broadcasting typically makes your code more concise and faster, so you should strive to use it where possible. Numpy DocumentationThis brief overview has touched on many of the important things that you need to know about numpy, but is far from complete. Check out the numpy reference to find out much more about numpy. SciPyNumpy provides a highperformance multidimensional array and basic tools to compute with and manipulate these arrays. SciPy builds on this, and provides a large number of functions that operate on numpy arrays and are useful for different types of scientific and engineering applications. The best way to get familiar with SciPy is to browse the documentation. We will highlight some parts of SciPy that you might find useful for this class. Image operationsSciPy provides some basic functions to work with images. For example, it has functions to read images from disk into numpy arrays, to write numpy arrays to disk as images, and to resize images. Here is a simple example that showcases these functions:
Left: The original image.
Right: The tinted and resized image.
MATLAB filesThe functions Distance between pointsSciPy defines some useful functions for computing distances between sets of points. The function
You can read all the details about this function in the documentation. A similar function ( MatplotlibMatplotlib is a plotting library.
In this section give a brief introduction to the PlottingThe most important function in matplotlib is
Running this code produces the following plot: With just a little bit of extra work we can easily plot multiple lines at once, and add a title, legend, and axis labels:
You can read much more about the SubplotsYou can plot different things in the same figure using the
You can read much more about the ImagesYou can use the
