x = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]

"""
Explanation: Python Data Structures
Data structure in computing
Data structures are how computer programs store information. Theses information can be processed, analyzed 
and visualized easily from the programme. Scientific data can be large and complex and may require data structures appropriate for scientific programming. In Astronomy, the fits file is one of the most widely used data-storing medium, it can store a lot of information including the coordinates, the precious time, a very large cataelog table, multi-dimension data cube, etc.. These data, when it is opened by the programme, shall be recognised and easily managed by the programme.
In Python, there are pre-defined advanced data structure depending on the kind of data you wish to store. 
You will have to choose data structures that best meet your requirements for the problem you are trying to solve. In this section, I will go through specifically examine three Python data structures: datetime, lists, tuples, sets, and dictionaries.
lists
A Python list is a sequence of values (elements) that are usually the same kind of item. They are in order and mutable. Mutable means they can be changed after they are created, of course, this implies you can exchange the order of the elements inside it. This is a Python list of prime numbers smaller than 100:
End of explanation
"""


print x
print x[0]

"""
Explanation: Definition
It is defined with parentheses : [xx,xx,xx]. 
Get Element
The elements are called using a square bracket with an index starting from zero : x[y], 0..N. 
Slice (sub-array)
You can slice the array using colon, in this case a[start:end] means items start up to end-1.
End of explanation
"""


print x[1:2]
print x[:]
print x[:2]
print x[1:]

"""
Explanation: A single colon a[:] means a copy of the whole array.
a[start:] return tuple of items start through the rest of the array.
a[:end]return tuple of items from the beginning through end-1.
End of explanation
"""


print x[-1]
print x[-2]
print x[-2:]
print x[:-2]

"""
Explanation: more interestingly, they have negative index
a[-1] means last item in the array
a[-2:] means last two items in the array
a[:-2] means everything except the last two items
End of explanation
"""


print x[::-1]

"""
Explanation: You may reversed a list with xxx[::-1].
End of explanation
"""


print x + [0,1]
print [0,1] + x
print [0,1] * 5

"""
Explanation: Concatenate
You may add up two list or we say concatenate, and multiply to duplicate the items.
End of explanation
"""


y = [97, 89, 83, 79, 73, 71, 67, 61, 59, 53, 47, 43, 41, 37, 31, 29, 23, 19, 17, 13, 11, 7, 5, 3, 2]
print y
z = sorted(y)
print y
print z

"""
Explanation: Sorting
You may sort a list with sorted(x). Noted that it returns a new list.
End of explanation
"""


print x
x.append('A')
print x

print x
x.insert(5,'B') # insert 'B' between x[4] and x[5], results in x[5] = 'B'
print x

print x; 
x.pop(5); # Removed the x[5] item and return it
print x; 
x.pop(-1); # Removed the last item and return it
print x

"""
Explanation: Add element (append); Remove element (pop); Insert element (insert)
These functions are modified in-place, i.e. the original list will be changed
End of explanation
"""


corr = (22.28552, 114.15769)
print corr

corr[0] = 10

"""
Explanation: More on lists
https://docs.python.org/2/tutorial/datastructures.html
Tuple
A Python tuple is similar to a list. The elements are in order but fixed once they are created. In other words, they are immutable. The tuple can store differently type of elements. 
Definition
It is defined with parentheses : (xx,xx,xx). 
Get Element
The elements are called using a square bracket with an index starting from zero : x[y], 0..N. 
Slice (sub-array)
You can slice the array using colon, in this case a[start:end] means items start up to end-1.
End of explanation
"""


# Length of the list/tuple
print len(x)
# Retrun the minimum and maximum in the list/tuple
print min(x), max(x)

# Multiple assignment
lat,lon = corr
print lat, lon

# String formating with Tuple unpacking with `*`
print 'lat {0}, lon {1}'.format(*corr)

# tuple to list, list to tuple
print list(corr)
print tuple(x)

"""
Explanation: Useful Functions for Lists and Tuples
End of explanation
"""


### More on slicing in list and tuple
start=2
end=5
step=2

print "Original:",x
print x[start:end] # items start through end-1
print x[start:]    # items start through the rest of the array
print x[:end]      # items from the beginning through end-1
print x[:]         # a copy of the whole array
print x[-1]    # last item in the array
print x[-2:]   # last two items in the array
print x[:-2]   # everything except the last two items

print x[start:end:step] # start through not past end, by step

"""
Explanation: More on slicing in list and tuple
End of explanation
"""


# Creating an empty dictionary
location = {}
print location

# Defined with a curly bracket
location = {
            'Berlin': (52.5170365, 13.3888599),
            'London': (51.5073219, -0.1276474),
            'Sydney': (-33.8548157, 151.2164539),
            'Tokyo': (34.2255804, 139.294774527387),
            'Paris': (48.8566101, 2.3514992),
            'Moscow': (46.7323875, -117.0001651)
           }
print location

# Update
location.update({'Hong Kong': (22.2793278, 114.1628131)})
print location

# Call element
print location['Tokyo']

# Delete element
del location['Hong Kong']
print location

for key, value in location.items():
    print value

"""
Explanation: Set
Dictionary
Dictionary is more flexible than list and its index is a string, it is defined with curly bracket: 
data = {'k1' : y1 , 'k2' : y2 , 'k3' : y3 }
k1,k2,k3 are called keys while y1,y2 and y3 are elements.
Creating an empty dictionary
It is defined with a pair of curly bracket or the dict() fuction: data = {} or data = dict()
Creating a dictionary with initial values

It could be defined with a curly bracket with index:element pairs : data = {'k1' : y1 , 'k2' : y2 , 'k3' : y3 }. 
It could also be defined with the dict() function : data = dict(k1=y1, k2=y2, k3=y3).
It could also be defined with tuples : data = {k: v for k, v in (('k1', y1),('k2',y2),('k3',y3))}.

Get Element
The elements are called using a square bracket with an index string : data[key]. 
Inserting/Updating a single value / multiple values


data['k1']=1  # Updates if 'k1' exists, else adds the element with index 'k1'


data.update({'k1':1})


data.update(dict(k1=1))


data.update(k1=1)


Multiple values : data.update({'k3':3,'k4':4})  # Updates 'k3' and adds 'k4'


Merged dictionary without modifying originals

data3 = {}
data3.update(data)  # Modifies data3, not data
data3.update(data2)  # Modifies data3, not data2

Delete an item

del data[key]  # Removes specific element in a dictionary
data.pop(key)  # Removes the key & returns the value
data.clear()  # Clears entire dictionary

Check if a key is existed

key in data # Return a boolean

Iterate through pairs

for key in data: # Iterates just through the keys, ignoring the values
for key, value in d.items(): # Iterates through the pairs
End of explanation
"""


import numpy as np

"""
Explanation: Numpy
<img src="images/numpylogo.svg" alt="matplotlib" style="width: 400px;"/>
Numpy is a numerical package used extensively in python coding. You can call the install the numpy package by 
pip install numpy
When you import a module, you can choose to bound an alias to the package. In python communities, we usually import the nupy module like this:
End of explanation
"""


a = np.array([2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97])

"""
Explanation: When you import a module via
python
import numpy
the numpy package is bound to the local variable numpy. The new syntax
python
import numpy as np
allows you to bind the import to the local variable name of your choice (usually to avoid name collisions, shorten verbose module names, or standardize access to modules with compatible APIs). The whole command is equivalent to:
python
import numpy
np = numpy
del numpy
numpy array
A numpy array is a grid of values, all of the same type. The number of dimensions give the rank of the array.
To initilze a 1D array, we will do:
End of explanation
"""


print a.shape
print a

"""
Explanation: The shape of an array is a tuple of integers giving the size of the array along each dimension.
End of explanation
"""


b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

print b.shape
print b

"""
Explanation: To initilze a 1D array, we can do:
End of explanation
"""


print a[0], a[3], a[5]

print b[0,0],b[1,1],b[2,2]

a[0] = 5
print a[0]
a[0] = 2

print b
b[0,0] = 2
print b

"""
Explanation: To call or change the element in the array, we can apply similar operation as a list:
End of explanation
"""


# Create an array of all zeros
a = np.zeros((2,2))   
print(a)

# Create an array of all ones
b = np.ones((5,5))   
print(b)

# Create a constant array
c = np.full((3,3), 7)  
print(c)

# Create a 3x3 identity matrix
d = np.eye(3)
print(d)

# Create an array filled with random values from 0 to 1
e = np.random.random((6,6))
print(e) 

# arange function in numpy provides convenient way to produce an array interpolate between the numbers, the input
# are start, stop, step, run %pdoc np.arange to check
print np.arange(-10,10,1)

# np.pi = pi, the resultant araay of this line outputs an interpolation from -pi/2 to pi/2, with 0.01 interval
print np.arange(-np.pi/2,np.pi/2,0.1)

"""
Explanation: Numpy also provides some alternative functions to initlize arrays:
End of explanation
"""


e = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print e
# Each dimensions slice similar to a list, here it means 1st dimension select all, 
# 2nd dimension select from one till end.
print e[:,1:]
# Here it means 1st dimension and 2nd dimension select from start till 2, 
# i.e. the upper left part of the array.
print e[:2,:2]

"""
Explanation: Array indexing
Slicing
End of explanation
"""


# An operation like numpy array > than a value return a boolean array
e = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print e > 5

# If we put the boolean array into the same array, itwill select all element that satisfy the conditions
e = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print e[e > 5]

"""
Explanation: Boolean array indexing
End of explanation
"""


# numpy sum can sum up all value in an array
e = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print e
print np.sum(e)

# Or along a dimension, here the operation sum up along the column (2nd dimension)
print np.sum(e,1)

# Or along a dimension, here the operation sum up along the row (1st dimension)
print np.sum(e,0)

# The .T returns the transpose of an array
print e.T

"""
Explanation: Array Operation
End of explanation
"""


