Python


Miscellaneous notes

a,x=x,a   x and a swap values

python memory management

indentation determines code blocks

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.
Stdo
input() actually evaluates the input as Python code. I suggest to never use it. raw_input() returns the verbatim string entered by the user.
name = raw_input("Enter your name: ")

Numbers: type()    +     -       /       *      ** (pow)      +=      *=  
Boolean:   type(),     True, False,   and    or    not     !=     ==


String

String + int      : err   --> string + str(int)
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 used for concatenation to string

str = "%s %s %d" % ("hello", "world", 12)    # formatting
str.strip()   # truncate



 i=0
while i <= 2 :
    i=i+1
list = [7,8,9]
for value in list:    
    print value
 for idx, val in enumerate(list): # to also know the index
     print '#%d: %s' % (idx + 1, list)
 
nums = [0, 1, 2, 3, 4]
squares = []
for x in nums:
    squares.append(x ** 2)

List comprehension
nums = [0, 1, 2, 3, 4]
squares = [x ** 2 for x in nums]

Dictionary comprehension
even_num_to_square = {x: x ** 2 for x in nums}






functions
def hello():
    print "hello"
    return 1234   # return 0,1   # to return tuples

Tuples/Lists/Dictionaries/Sets
0-based

Tuples
months = ('January','February','March')
months[0] # january

lists are like tuples but their values can change and are point by reference
p = list()
p = ["a", 3, "c", [3, "A"]]
p[1]
p[-1] # last item
p[2:]  # 2 to end
p[:2]  # end to 2
p.append([4, 5])    # ["a", 3, "c", [3, "A"], [4,5]]     #  Appends object at end.
p.extend([4,5])       # ["a", 3, "c", [3, "A"], 4,5]      # Extends list by appending elements from the iterable.  p.extend("hello")   #  ["a", 3, "c", [3, "A"], 4,5, h, e, l , l, o]
del p[1]
p[2:3]      # [3, "c"]
p[1]=1
num1 + num2
len(num)
nums.index(num)            # index or error
value in LIST  ----  value not in LIST
LIST.pop() remove and return last element

nums = range(5)

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 k, v in d.iteritems():
   print

# Sort dictionary by values and get keys list ordered by values
sorted_keys = sorted(my_dict, key=my_dict.get, reverse=True)

my_dict.get('a key', a_default_value_if_not_found)

set

a = {1,3,3,3,4}   # a becomes {1,3,4}
from list:
myset = set(["mort", "b", "g", "g"])     # ['b', 'g', 'mort']

Iterable, generator
iterable: anything you can call "for ... in ..."  is an iterable. list, string, set, file .... Everything is in memory, iterate as many times as you want
>>> mylist = [x*x for x in range(3)]
>>> for i in mylist:
...    print(i)
0
1
4
generator: is an iterable where each item is brought to memory only once.
>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator:
...    print(i)
0
1
4
It is just the same except you used () instead of []. BUT, you cannot perform for i in mygenerator a second time since generators can only be used once: they calculate 0, then forget about it and calculate 1, and end calculating 4, one by one.

yield: yield is a keyword that is used like return, except the function will return a generator.
 >>> def createGenerator():
...    mylist = range(3)
...    for i in mylist:
...        yield i*i
...
>>> mygenerator = createGenerator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object createGenerator at 0xb7555c34>
>>> for i in mygenerator:
...     print(i)
0
1
4

Here it's a useless example, but it's handy when you know your function will return a huge set of values that you will only need to read once.

To master yield, you must understand that when you call the function, the code you have written in the function body does not run. The function only returns the generator object, this is a bit tricky :-)

Then, your code will be run each time the for uses the generator.

Now the hard part:

The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield, then it'll return the first value of the loop. Then, each other call will run the loop you have written in the function one more time, and return the next value, until there is no value to return.

The generator is considered empty once the function runs but does not hit yield anymore. It can be because the loop had come to an end, or because you do not satisfy an "if/else" anymore.


def f123():
    yield 1
    yield 2
    yield 3

for item in f123():      # generator gives a iterable. for loop runs until yield then suspends function and returns value. next calls do the same thing on the iterable
    print item

Classes

class Greeter(parent_class):          # or [parent classes]
  my_class_attr = 0        # a class-level attribute
  # Constructor 
  def __init__(self, name): 
    self.name = name # an instance variable 
    def greet(self, otherparams)   # Instance method always starts with *self* and then other params
      print("Hi " + name)
 
g = Greeter('Fred')
g.greet()

Other utility functions
hasattr(emp1, 'age') 
getattr(emp1, 'age') 
setattr(emp1, 'age', 8) 
delattr(empl, 'age')

issubclass(sub, sup)
isinstance(obj, Class) 

def __add__(self,other): # overrides the + operator to include this class instances


Built-In Class Attributes
__dict__: Dictionary containing the class's namespace.
__doc__: Class documentation string or none, if undefined.
__name__: Class name.
__module__: Module name in which the class is defined. This attribute is "__main__" in interactive mode.
__bases__: A possibly empty tuple containing the base classes, in the order of their occurrence in the base class list.
__del__(), called a destructor, that is invoked when the instance is about to be destroyed.


Undescore Meaning
__foo__: this is just a convention, a way for the Python system to use names that won't conflict with user names. 
_foo: this is just a convention, a way for the programmer to indicate that the variable is private (whatever that means in Python). 
__foo: this has real meaning: the interpreter replaces this name with _classname__foo as a way to ensure that the name will not overlap with a similar name in another class. 
No other form of underscores have meaning in the Python world.



Modules
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.

moduletest.py
a = 1

def printHi():
    print("Hi")

main.py
import moduletest   # always have to precede with module name
print moduletest.a

OR
from moduletest import printHi    # can use directly
printHi()

IO

Text File
with open('/Users/mshahriarinia/Documents/ner/ner-testing.json') as f:
    for line in f:
        <do something with line>

Save Objects (Pickles)
import pickle

picklelist = ['one',2,'three','four',5,'can you count?']  # pickle this list
file = open('filename', 'w')
pickle.dump(picklelist,file)
file.close()
 import pickle

unpicklefile = open('filename', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()

for item in unpickledlist:
    print item


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
    except err:
        pass







Python Numpy



Sets

A set is an unordered collection of distinct elements. As a simple example, consider the following:

animals = {'cat', 'dog'}
print 'cat' in animals   # Check if an element is in a set; prints "True"
print 'fish' in animals  # prints "False"
animals.add('fish')      # Add an element to a set
print 'fish' in animals  # Prints "True"
print len(animals)       # Number of elements in a set; prints "3"
animals.add('cat')       # Adding an element that is already in the set does nothing
print len(animals)       # Prints "3"
animals.remove('cat')    # Remove an element from a set
print len(animals)       # Prints "2"

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:

animals = {'cat', 'dog', 'fish'}
for idx, animal in enumerate(animals):
    print '#%d: %s' % (idx + 1, animal)
# Prints "#1: fish", "#2: dog", "#3: cat"

Set comprehensions: Like lists and dictionaries, we can easily construct sets using set comprehensions:

from math import sqrt
nums = {int(sqrt(x)) for x in range(30)}
print nums  # Prints "set([0, 1, 2, 3, 4, 5])"

Tuples

A 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:

d = {(x, x + 1): x for x in range(10)}  # Create a dictionary with tuple keys
t = (5, 6)       # Create a tuple
print type(t)    # Prints "<type 'tuple'>"
print d[t]       # Prints "5"
print d[(1, 2)]  # Prints "1"

The documentation has more information about tuples.

Functions

Python functions are defined using the def keyword. For example:

def sign(x):
    if x > 0:
        return 'positive'
    elif x < 0:
        return 'negative'
    else:
        return 'zero'

for x in [-1, 0, 1]:
    print sign(x)
# Prints "negative", "zero", "positive"

We will often define functions to take optional arguments, like this:

def hello(name, loud=False):
    if loud:
        print 'HELLO, %s' % name.upper()
    else:
        print 'Hello, %s!' % name

hello('Bob') # Prints "Hello, Bob"
hello('Fred', loud=True)  # Prints "HELLO, FRED!"

There is a lot more information about Python classes in the documentation.

.

Numpy

Numpy is the core library for scientific computing in Python. It provides a high-performance 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.

Arrays

A 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:

import numpy as np

a = np.array([1, 2, 3])  # Create a rank 1 array
print type(a)            # Prints "<type 'numpy.ndarray'>"
print a.shape            # Prints "(3,)"
print a[0], a[1], a[2]   # Prints "1 2 3"
a[0] = 5                 # Change an element of the array
print a                  # Prints "[5, 2, 3]"

b = np.array([[1,2,3],[4,5,6]])   # Create a rank 2 array
print b.shape                     # Prints "(2, 3)"
print b[0, 0], b[0, 1], b[1, 0]   # Prints "1 2 4"

Numpy also provides many functions to create arrays:

import numpy as np

a = np.zeros((2,2))  # Create an array of all zeros
print a              # Prints "[[ 0.  0.]
                     #          [ 0.  0.]]"

b = np.ones((1,2))   # Create an array of all ones
print b              # Prints "[[ 1.  1.]]"

c = np.full((2,2), 7) # Create a constant array
print c               # Prints "[[ 7.  7.]
                      #          [ 7.  7.]]"

d = np.eye(2)        # Create a 2x2 identity matrix
print d              # Prints "[[ 1.  0.]
                     #          [ 0.  1.]]"

e = np.random.random((2,2)) # Create an array filled with random values
print e                     # Might print "[[ 0.91940167  0.08143941]
                            #               [ 0.68744134  0.87236687]]"

You can read about other methods of array creation in the documentation.

Array indexing

Numpy 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:

import numpy as np

# Create the following rank 2 array with shape (3, 4)
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

# Use slicing to pull out the subarray consisting of the first 2 rows
# and columns 1 and 2; b is the following array of shape (2, 2):
# [[2 3]
#  [6 7]]
b = a[:2, 1:3]

# A slice of an array is a view into the same data, so modifying it
# will modify the original array.
print a[0, 1]   # Prints "2"
b[0, 0] = 77    # b[0, 0] is the same piece of data as a[0, 1]
print a[0, 1]   # Prints "77"

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:

import numpy as np

# Create the following rank 2 array with shape (3, 4)
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

# Two ways of accessing the data in the middle row of the array.
# Mixing integer indexing with slices yields an array of lower rank,
# while using only slices yields an array of the same rank as the
# original array:
row_r1 = a[1, :]    # Rank 1 view of the second row of a  
row_r2 = a[1:2, :]  # Rank 2 view of the second row of a
print row_r1, row_r1.shape  # Prints "[5 6 7 8] (4,)"
print row_r2, row_r2.shape  # Prints "[[5 6 7 8]] (1, 4)"

# We can make the same distinction when accessing columns of an array:
col_r1 = a[:, 1]
col_r2 = a[:, 1:2]
print col_r1, col_r1.shape  # Prints "[ 2  6 10] (3,)"
print col_r2, col_r2.shape  # Prints "[[ 2]
                            #          [ 6]
                            #          [10]] (3, 1)"

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:

import numpy as np

a = np.array([[1,2], [3, 4], [5, 6]])

# An example of integer array indexing.
# The returned array will have shape (3,) and 
print a[[0, 1, 2], [0, 1, 0]]  # Prints "[1 4 5]"  The first array is the row # and the second array is the col #

# The above example of integer array indexing is equivalent to this:
print np.array([a[0, 0], a[1, 1], a[2, 0]])  # Prints "[1 4 5]"


# When using integer array indexing, you can reuse the same
# element from the source array:
print a[[0, 0], [1, 1]]  # Prints "[2 2]"

# Equivalent to the previous integer array indexing example
print np.array([a[0, 1], a[0, 1]])  # Prints "[2 2]"

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:

import numpy as np

a = np.array([[1,2], [3, 4], [5, 6]])

bool_idx = (a > 2)  # Find the elements of a that are bigger than 2;
                    # this returns a numpy array of Booleans of the same
                    # shape as a, where each slot of bool_idx tells
                    # whether that element of a is > 2.

print bool_idx      # Prints "[[False False]
                    #          [ True  True]
                    #          [ True  True]]"

# We use boolean array indexing to construct a rank 1 array
# consisting of the elements of a corresponding to the True values
# of bool_idx
print a[bool_idx]  # Prints "[3 4 5 6]"

# We can do all of the above in a single concise statement:
print a[a > 2]     # Prints "[3 4 5 6]"

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.

Datatypes

Every 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:

import numpy as np

x = np.array([1, 2])  # Let numpy choose the datatype
print x.dtype         # Prints "int64"

x = np.array([1.0, 2.0])  # Let numpy choose the datatype
print x.dtype             # Prints "float64"

x = np.array([1, 2], dtype=np.int64)  # Force a particular datatype
print x.dtype                         # Prints "int64"

You can read all about numpy datatypes in the documentation.

Array math

Basic mathematical functions operate elementwise on arrays, and are available both as operator overloads and as functions in the numpy module:

import numpy as np

x = np.array([[1,2],[3,4]], dtype=np.float64)
y = np.array([[5,6],[7,8]], dtype=np.float64)

# Elementwise sum; both produce the array
# [[ 6.0  8.0]
#  [10.0 12.0]]
print x + y
print np.add(x, y)

# Elementwise difference; both produce the array
# [[-4.0 -4.0]
#  [-4.0 -4.0]]
print x - y
print np.subtract(x, y)

# Elementwise product; both produce the array
# [[ 5.0 12.0]
#  [21.0 32.0]]
print x * y
print np.multiply(x, y)

# Elementwise division; both produce the array
# [[ 0.2         0.33333333]
#  [ 0.42857143  0.5       ]]
print x / y
print np.divide(x, y)

# Elementwise square root; produces the array
# [[ 1.          1.41421356]
#  [ 1.73205081  2.        ]]
print np.sqrt(x)

Note that unlike MATLAB, * is elementwise multiplication, not matrix multiplication. We instead use the dot function to compute inner products of vectors, to multiply a vector by a matrix, and to multiply matrices. dot is available both as a function in the numpy module and as an instance method of array objects:

import numpy as np

x = np.array([[1,2],[3,4]])
y = np.array([[5,6],[7,8]])

v = np.array([9,10])
w = np.array([11, 12])

# Inner product of vectors; both produce 219
print v.dot(w)
print np.dot(v, w)

# Matrix / vector product; both produce the rank 1 array [29 67]
print x.dot(v)
print np.dot(x, v)

# Matrix / matrix product; both produce the rank 2 array
# [[19 22]
#  [43 50]]
print x.dot(y)
print np.dot(x, y)

Numpy provides many useful functions for performing computations on arrays; one of the most useful is sum:

import numpy as np

x = np.array([[1,2],[3,4]])

print np.sum(x)  # Compute sum of all elements; prints "10"
print np.sum(x, axis=0)  # Compute sum of each column; prints "[4 6]"
print np.sum(x, axis=1)  # Compute sum of each row; prints "[3 7]"

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 T attribute of an array object:

import numpy as np

x = np.array([[1,2], [3,4]])
print x    # Prints "[[1 2]
           #          [3 4]]"
print x.T  # transpose: Prints "[[1 3]
           #          [2 4]]"

# Note that taking the transpose of a rank 1 array does nothing:
v = np.array([1,2,3])
print v    # Prints "[1 2 3]"
print v.T  # Prints "[1 2 3]"

Numpy provides many more functions for manipulating arrays; you can see the full list in the documentation.

Broadcasting

Broadcasting 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:

import numpy as np

# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = np.empty_like(x)   # Create an empty matrix with the same shape as x

# Add the vector v to each row of the matrix x with an explicit loop
for i in range(4):
    y[i, :] = x[i, :] + v

# Now y is the following
# [[ 2  2  4]
#  [ 5  5  7]
#  [ 8  8 10]
#  [11 11 13]]
print y

This works; however when the matrix x is very large, computing an explicit loop in Python could be slow. Note that adding the vector v to each row of the matrix x is equivalent to forming a matrix vv by stacking multiple copies of v vertically, then performing elementwise summation of x and vv. We could implement this approach like this:

import numpy as np

# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
vv = np.tile(v, (4, 1))  # Stack 4 copies of v on top of each other
print vv                 # Prints "[[1 0 1]
                         #          [1 0 1]
                         #          [1 0 1]
                         #          [1 0 1]]"
y = x + vv  # Add x and vv elementwise
print y  # Prints "[[ 2  2  4
         #          [ 5  5  7]
         #          [ 8  8 10]
         #          [11 11 13]]"

Numpy broadcasting allows us to perform this computation without actually creating multiple copies of v. Consider this version, using broadcasting:

import numpy as np

# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = x + v  # Add v to each row of x using broadcasting
print y  # Prints "[[ 2  2  4]
         #          [ 5  5  7]
         #          [ 8  8 10]
         #          [11 11 13]]"

The line y = x + v works even though x has shape (4, 3) and v has shape (3,) due to broadcasting; this line works as if v actually had shape (4, 3), where each row was a copy of v, and the sum was performed elementwise.

Broadcasting two arrays together follows these rules:

  1. If the arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.
  2. The two arrays are said to be compatible in a dimension if they have the same size in the dimension, or if one of the arrays has size 1 in that dimension.
  3. The arrays can be broadcast together if they are compatible in all dimensions.
  4. After broadcasting, each array behaves as if it had shape equal to the elementwise maximum of shapes of the two input arrays.
  5. In any dimension where one array had size 1 and the other array had size greater than 1, the first array behaves as if it were copied along that dimension

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:

import numpy as np

# Compute outer product of vectors
v = np.array([1,2,3])  # v has shape (3,)
w = np.array([4,5])    # w has shape (2,)
# To compute an outer product, we first reshape v to be a column
# vector of shape (3, 1); we can then broadcast it against w to yield
# an output of shape (3, 2), which is the outer product of v and w:
# [[ 4  5]
#  [ 8 10]
#  [12 15]]
print np.reshape(v, (3, 1)) * w

# Add a vector to each row of a matrix
x = np.array([[1,2,3], [4,5,6]])
# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),
# giving the following matrix:
# [[2 4 6]
#  [5 7 9]]
print x + v

# Add a vector to each column of a matrix
# x has shape (2, 3) and w has shape (2,).
# If we transpose x then it has shape (3, 2) and can be broadcast
# against w to yield a result of shape (3, 2); transposing this result
# yields the final result of shape (2, 3) which is the matrix x with
# the vector w added to each column. Gives the following matrix:
# [[ 5  6  7]
#  [ 9 10 11]]
print (x.T + w).T
# Another solution is to reshape w to be a row vector of shape (2, 1);
# we can then broadcast it directly against x to produce the same
# output.
print x + np.reshape(w, (2, 1))

# Multiply a matrix by a constant:
# x has shape (2, 3). Numpy treats scalars as arrays of shape ();
# these can be broadcast together to shape (2, 3), producing the
# following array:
# [[ 2  4  6]
#  [ 8 10 12]]
print x * 2

Broadcasting typically makes your code more concise and faster, so you should strive to use it where possible.

Numpy Documentation

This 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.

SciPy

Numpy provides a high-performance 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 operations

SciPy 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:

from scipy.misc import imread, imsave, imresize

# Read an JPEG image into a numpy array
img = imread('assets/cat.jpg')
print img.dtype, img.shape  # Prints "uint8 (400, 248, 3)"

# We can tint the image by scaling each of the color channels
# by a different scalar constant. The image has shape (400, 248, 3);
# we multiply it by the array [1, 0.95, 0.9] of shape (3,);
# numpy broadcasting means that this leaves the red channel unchanged,
# and multiplies the green and blue channels by 0.95 and 0.9
# respectively.
img_tinted = img * [1, 0.95, 0.9]

# Resize the tinted image to be 300 by 300 pixels.
img_tinted = imresize(img_tinted, (300, 300))

# Write the tinted image back to disk
imsave('assets/cat_tinted.jpg', img_tinted)
Left: The original image. Right: The tinted and resized image.

MATLAB files

The functions scipy.io.loadmat and scipy.io.savemat allow you to read and write MATLAB files. You can read about them in the documentation.

Distance between points

SciPy defines some useful functions for computing distances between sets of points.

The function scipy.spatial.distance.pdist computes the distance between all pairs of points in a given set:

import numpy as np
from scipy.spatial.distance import pdist, squareform

# Create the following array where each row is a point in 2D space:
# [[0 1]
#  [1 0]
#  [2 0]]
x = np.array([[0, 1], [1, 0], [2, 0]])
print x

# Compute the Euclidean distance between all rows of x.
# d[i, j] is the Euclidean distance between x[i, :] and x[j, :],
# and d is the following array:
# [[ 0.          1.41421356  2.23606798]
#  [ 1.41421356  0.          1.        ]
#  [ 2.23606798  1.          0.        ]]
d = squareform(pdist(x, 'euclidean'))
print d

You can read all the details about this function in the documentation.

A similar function (scipy.spatial.distance.cdist) computes the distance between all pairs across two sets of points; you can read about it in the documentation.

Matplotlib

Matplotlib is a plotting library. In this section give a brief introduction to the matplotlib.pyplot module, which provides a plotting system similar to that of MATLAB.

Plotting

The most important function in matplotlib is plot, which allows you to plot 2D data. Here is a simple example:

import numpy as np
import matplotlib.pyplot as plt

# Compute the x and y coordinates for points on a sine curve
x = np.arange(0, 3 * np.pi, 0.1)
y = np.sin(x)

# Plot the points using matplotlib
plt.plot(x, y)
plt.show()  # You must call plt.show() to make graphics appear.

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:

import numpy as np
import matplotlib.pyplot as plt

# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)

# Plot the points using matplotlib
plt.plot(x, y_sin)
plt.plot(x, y_cos)
plt.xlabel('x axis label')
plt.ylabel('y axis label')
plt.title('Sine and Cosine')
plt.legend(['Sine', 'Cosine'])
plt.show()

You can read much more about the plot function in the documentation.

Subplots

You can plot different things in the same figure using the subplot function. Here is an example:

import numpy as np
import matplotlib.pyplot as plt

# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)

# Set up a subplot grid that has height 2 and width 1,
# and set the first such subplot as active.
plt.subplot(2, 1, 1)

# Make the first plot
plt.plot(x, y_sin)
plt.title('Sine')

# Set the second subplot as active, and make the second plot.
plt.subplot(2, 1, 2)
plt.plot(x, y_cos)
plt.title('Cosine')

# Show the figure.
plt.show()

You can read much more about the subplot function in the documentation.

Images

You can use the imshow function to show images. Here is an example:

import numpy as np
from scipy.misc import imread, imresize
import matplotlib.pyplot as plt

img = imread('assets/cat.jpg')
img_tinted = img * [1, 0.95, 0.9]

# Show the original image
plt.subplot(1, 2, 1)
plt.imshow(img)

# Show the tinted image
plt.subplot(1, 2, 2)

# A slight gotcha with imshow is that it might give strange results
# if presented with data that is not uint8. To work around this, we
# explicitly cast the image to uint8 before displaying it.
plt.imshow(np.uint8(img_tinted))
plt.show()




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/dist-packages


ssh msnia@freeplay-02.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@freeplay-02.cise.ufl.edu:pysrc
ssh msnia@freeplay-02.cise.ufl.edu 'sudo cp -R pysrc/* /usr/local/lib/python2.7/dist-packages/'
ssh msnia@freeplay-02.cise.ufl.edu rm -r pysrc


Configure path mapping. On the remote machine, edit /usr/local/lib/python2.7/dist-packages/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/nfs-vagrant'  
PATHS_FROM_ECLIPSE_TO_PYTHON = [(r''/Users/morteza/zproject/workspaces/spokenlanguageunderstanding, r'/home/msnia/zproject/spokenlanguageunderstanding'), ]
Subpages (2): Jupyter Notebook NumPy
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