1666522200
Fortran-like Arrays with Arbitrary, Zero Or Negative Starting indices
OffsetArrays.jl
Introduction
OffsetArrays provides Julia users with arrays that have arbitrary indices, similar to those found in some other programming languages like Fortran.
An OffsetArray is a lightweight wrapper around an AbstractArray that shifts its indices. Generally, indexing into an OffsetArray should be as performant as the parent array.
Usage
There are two ways to construct OffsetArrays: by specifying the axes of the array, or by specifying its origin.
The first way to construct an OffsetArray by specifying its axes is:
OA = OffsetArray(A, axis1, axis2, ...)
where you want OA to have axes (axis1, axis2, ...) and be indexed by values that fall within these axis ranges. Example:
julia> using OffsetArrays
julia> A = Float64.(reshape(1:15, 3, 5))
3×5 Matrix{Float64}:
1.0 4.0 7.0 10.0 13.0
2.0 5.0 8.0 11.0 14.0
3.0 6.0 9.0 12.0 15.0
julia> axes(A) # indices of a Matrix start from 1 along each axis
(Base.OneTo(3), Base.OneTo(5))
julia> OA = OffsetArray(A, -1:1, 0:4) # OA will have the axes (-1:1, 0:4)
3×5 OffsetArray(::Matrix{Float64}, -1:1, 0:4) with eltype Float64 with indices -1:1×0:4:
1.0 4.0 7.0 10.0 13.0
2.0 5.0 8.0 11.0 14.0
3.0 6.0 9.0 12.0 15.0
julia> OA[-1, 0]
1.0
julia> OA[1, 4]
15.0
The second way to construct an OffsetArray is by specifying the origin, that is, the first index along each axis. This is particularly useful if one wants, eg., arrays that are 0-indexed as opposed to 1-indexed.
A convenient way to construct an OffsetArray this way is by using OffsetArrays.Origin:
julia> using OffsetArrays: Origin
julia> Origin(0)(A) # indices begin at 0 along all axes
3×5 OffsetArray(::Matrix{Float64}, 0:2, 0:4) with eltype Float64 with indices 0:2×0:4:
1.0 4.0 7.0 10.0 13.0
2.0 5.0 8.0 11.0 14.0
3.0 6.0 9.0 12.0 15.0
julia> Origin(2, 3)(A) # indices begin at 2 along the first axis and 3 along the second
3×5 OffsetArray(::Matrix{Float64}, 2:4, 3:7) with eltype Float64 with indices 2:4×3:7:
1.0 4.0 7.0 10.0 13.0
2.0 5.0 8.0 11.0 14.0
3.0 6.0 9.0 12.0 15.0
While the examples here refer to the common case where the parent arrays have indices starting at 1, this is not necessary. An OffsetArray may wrap any array that has integer indices, irrespective of where the indices begin.
How to go back to 1-indexed arrays
Certain libraries, such as LinearAlgebra, require arrays to be indexed from 1. Passing an OffsetArray with shifted indices would lead to an error here.
julia> A = Float64.(reshape(1:16, 4, 4));
julia> AO = Origin(0)(A);
julia> using LinearAlgebra
julia> Diagonal(AO)
ERROR: ArgumentError: offset arrays are not supported but got an array with index other than 1
The way to obtain a 1-indexed array from an OffsetArray is by using OffsetArrays.no_offset_view.
An example of this is:
julia> OffsetArrays.no_offset_view(AO)
4×4 Matrix{Float64}:
1.0 5.0 9.0 13.0
2.0 6.0 10.0 14.0
3.0 7.0 11.0 15.0
4.0 8.0 12.0 16.0
This may now be passed to LinearAlgebra:
julia> D = Diagonal(OffsetArrays.no_offset_view(AO))
4×4 Diagonal{Float64, Vector{Float64}}:
1.0 ⋅ ⋅ ⋅
⋅ 6.0 ⋅ ⋅
⋅ ⋅ 11.0 ⋅
⋅ ⋅ ⋅ 16.0
If we want to restore the original indices of AO, we may wrap an OffsetArray around the Diagonal as:
julia> Origin(AO)(D)
4×4 OffsetArray(::Diagonal{Float64, Vector{Float64}}, 0:3, 0:3) with eltype Float64 with indices 0:3×0:3:
1.0 ⋅ ⋅ ⋅
⋅ 6.0 ⋅ ⋅
⋅ ⋅ 11.0 ⋅
⋅ ⋅ ⋅ 16.0
Here, Origin(AO) is able to automatically infer and use the indices of AO.
Best practice on adopting OffsetArrays
For some applications, OffsetArrays give users an easy-to-understand interface. However, handling the non-conventional axes of OffsetArrays requires extra care. Otherwise, the code might error, crash, or return incorrect results. You can read the Julialang documentation on offset for more information. Here we briefly summarize some of the best practices for users and package authors.
There is no need to support OffsetArrays for every function
You don't need to support offset arrays for internal functions that only consume standard 1-based arrays -- it doesn't change or improve anything.
You don't need to support offset arrays for functions that have no well-defined behavior on custom axes. For instance, many linear algebra functions such as matrix multiplication A * B does not have an agreed behavior for offset arrays. In this case, it is a better practice to let users do the conversion.
The helper function Base.require_one_based_indexing can be used to early check the axes and throw a meaningful error. If your interface functions do not intend to support offset arrays, we recommend you add this check before starting the real computation.
use axes instead of size/length
Many implementations assume the array axes start at 1 by writing loops such as for i in 1:length(x) or for i in 1:size(x, 1). A better practice is to use for i in eachindex(x) or for i in axes(x, 1) -- axes provides more information than size with no performance overhead.
Also, if you know what indices type you want to use, LinearIndices and CartesianIndices allow you to loop multidimensional arrays efficiently without worrying about the axes.
test against OffsetArrays
For package authors that declare support for AbstractArray, we recommend having a few test cases against OffsetArray to ensure the function works well for arrays with custom axes. This gives you more confidence that users don't run into strange situations.
For package users that want to use offset arrays, many numerical correctness issues come from the fact that @inbounds is used inappropriately with the 1-based indexing assumption. Thus for debug purposes, it is not a bad idea to start Julia with --check-bounds=yes, which turns all @inbounds into a no-op and uncover potential out-of-bound errors.
Download Details:
Author: JuliaArrays
Source Code: https://github.com/JuliaArrays/OffsetArrays.jl
License: View license
What is GEEK
Buddha Community
1670560264
Understanding Arrays in Python
Learn how to use Python arrays. Create arrays in Python using the array module. You'll see how to define them and the different methods commonly used for performing operations on them.
The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.
Table of Contents
- Introduction to Arrays
- The differences between Lists and Arrays
- When to use arrays
- How to use arrays
- Define arrays
- Find the length of arrays
- Array indexing
- Search through arrays
- Loop through arrays
- Slice an array
- Array methods for performing operations
- Change an existing value
- Add a new value
- Remove a value
- Conclusion
Let's get started!
What are Python Arrays?
Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.
Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.
What's the Difference between Python Lists and Python Arrays?
Lists are one of the most common data structures in Python, and a core part of the language.
Lists and arrays behave similarly.
Just like arrays, lists are an ordered sequence of elements.
They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.
However, lists and arrays are not the same thing.
Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.
As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.
When to Use Python Arrays
Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.
Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.
They are also more compact and take up less memory and space which makes them more size efficient compared to lists.
If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.
How to Use Arrays in Python
In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.
There are three ways you can import the array module:
- By using
import arrayat the top of the file. This includes the modulearray. You would then go on to create an array usingarray.array().
import array
#how you would create an array
array.array()
- Instead of having to type
array.array()all the time, you could useimport array as arrat the top of the file, instead ofimport arrayalone. You would then create an array by typingarr.array(). Thearracts as an alias name, with the array constructor then immediately following it.
import array as arr
#how you would create an array
arr.array()
- Lastly, you could also use
from array import *, with*importing all the functionalities available. You would then create an array by writing thearray()constructor alone.
from array import *
#how you would create an array
array()
How to Define Arrays in Python
Once you've imported the array module, you can then go on to define a Python array.
The general syntax for creating an array looks like this:
variable_name = array(typecode,[elements])
Let's break it down:
variable_namewould be the name of the array.- The
typecodespecifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type. - Inside square brackets you mention the
elementsthat would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writingvariable_name = array(typecode)alone, without any elements.
Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:
| TYPECODE | C TYPE | PYTHON TYPE | SIZE |
|---|---|---|---|
| 'b' | signed char | int | 1 |
| 'B' | unsigned char | int | 1 |
| 'u' | wchar_t | Unicode character | 2 |
| 'h' | signed short | int | 2 |
| 'H' | unsigned short | int | 2 |
| 'i' | signed int | int | 2 |
| 'I' | unsigned int | int | 2 |
| 'l' | signed long | int | 4 |
| 'L' | unsigned long | int | 4 |
| 'q' | signed long long | int | 8 |
| 'Q' | unsigned long long | int | 8 |
| 'f' | float | float | 4 |
| 'd' | double | float | 8 |
Tying everything together, here is an example of how you would define an array in Python:
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers)
#output
#array('i', [10, 20, 30])
Let's break it down:
- First we included the array module, in this case with
import array as arr. - Then, we created a
numbersarray. - We used
arr.array()because ofimport array as arr. - Inside the
array()constructor, we first includedi, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, withHfor example, would mean that no negative values are allowed. - Lastly, we included the values to be stored in the array in square brackets.
Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:
import array as arr
numbers = arr.array('i',[10.0,20,30])
print(numbers)
#output
#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
# numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer
In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.
Another way to create an array is the following:
from array import *
#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])
print(numbers)
#output
#array('d', [10.0, 20.0, 30.0])
The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.
How to Find the Length of an Array in Python
To find out the exact number of elements contained in an array, use the built-in len() method.
It will return the integer number that is equal to the total number of elements in the array you specify.
import array as arr
numbers = arr.array('i',[10,20,30])
print(len(numbers))
#output
# 3
In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.
Array Indexing and How to Access Individual Items in an Array in Python
Each item in an array has a specific address. Individual items are accessed by referencing their index number.
Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.
To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.
The general syntax would look something like this:
array_name[index_value_of_item]
Here is how you would access each individual element in an array:
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element
#output
#10
#20
#30
Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.
Note that you can also access each individual element using negative indexing.
With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.
Here is how you would get each item in an array using that method:
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
#output
#30
#20
#10
How to Search Through an Array in Python
You can find out an element's index number by using the index() method.
You pass the value of the element being searched as the argument to the method, and the element's index number is returned.
import array as arr
numbers = arr.array('i',[10,20,30])
#search for the index of the value 10
print(numbers.index(10))
#output
#0
If there is more than one element with the same value, the index of the first instance of the value will be returned:
import array as arr
numbers = arr.array('i',[10,20,30,10,20,30])
#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))
#output
#0
How to Loop through an Array in Python
You've seen how to access each individual element in an array and print it out on its own.
You've also seen how to print the array, using the print() method. That method gives the following result:
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers)
#output
#array('i', [10, 20, 30])
What if you want to print each value one by one?
This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.
For this you can use a simple for loop:
import array as arr
numbers = arr.array('i',[10,20,30])
for number in numbers:
print(number)
#output
#10
#20
#30
You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:
import array as arr
values = arr.array('i',[10,20,30])
#prints each individual value in the array
for value in range(len(values)):
print(values[value])
#output
#10
#20
#30
How to Slice an Array in Python
To access a specific range of values inside the array, use the slicing operator, which is a colon :.
When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#get the values 10 and 20 only
print(numbers[:2]) #first to second position
#output
#array('i', [10, 20])
When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#get the values 20 and 30 only
print(numbers[1:3]) #second to third position
#output
#rray('i', [20, 30])
Methods For Performing Operations on Arrays in Python
Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.
Let's see some of the most commonly used methods which are used for performing operations on arrays.
How to Change the Value of an Item in an Array
You can change the value of a specific element by speficying its position and assigning it a new value:
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40
print(numbers)
#output
#array('i', [40, 20, 30])
How to Add a New Value to an Array
To add one single value at the end of an array, use the append() method:
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 to the end of numbers
numbers.append(40)
print(numbers)
#output
#array('i', [10, 20, 30, 40])
Be aware that the new item you add needs to be the same data type as the rest of the items in the array.
Look what happens when I try to add a float to an array of integers:
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 to the end of numbers
numbers.append(40.0)
print(numbers)
#output
#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
# numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer
But what if you want to add more than one value to the end an array?
Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets
numbers.extend([40,50,60])
print(numbers)
#output
#array('i', [10, 20, 30, 40, 50, 60])
And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.
The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 in the first position
#remember indexing starts at 0
numbers.insert(0,40)
print(numbers)
#output
#array('i', [40, 10, 20, 30])
How to Remove a Value from an Array
To remove an element from an array, use the remove() method and include the value as an argument to the method.
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
numbers.remove(10)
print(numbers)
#output
#array('i', [20, 30])
With remove(), only the first instance of the value you pass as an argument will be removed.
See what happens when there are more than one identical values:
import array as arr
#original array
numbers = arr.array('i',[10,20,30,10,20])
numbers.remove(10)
print(numbers)
#output
#array('i', [20, 30, 10, 20])
Only the first occurence of 10 is removed.
You can also use the pop() method, and specify the position of the element to be removed:
import array as arr
#original array
numbers = arr.array('i',[10,20,30,10,20])
#remove the first instance of 10
numbers.pop(0)
print(numbers)
#output
#array('i', [20, 30, 10, 20])
Conclusion
And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.
You'll start from the basics and learn in an interacitve and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.
Thanks for reading and happy coding!
Original article source at https://www.freecodecamp.org
#python
1666082925
How to Create Arrays in Python
In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.
This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:
1:15 What is an array?
2:53 Is python list same as an array?
3:48 How to create arrays in python?
7:19 Accessing array elements
9:59 Basic array operations
- 10:33 Finding the length of an array
- 11:44 Adding Elements
- 15:06 Removing elements
- 18:32 Array concatenation
- 20:59 Slicing
- 23:26 Looping
Python Array Tutorial – Define, Index, Methods
In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.
The artcile covers arrays that you create by importing the array module. We won't cover NumPy arrays here.
Table of Contents
- Introduction to Arrays
- The differences between Lists and Arrays
- When to use arrays
- How to use arrays
- Define arrays
- Find the length of arrays
- Array indexing
- Search through arrays
- Loop through arrays
- Slice an array
- Array methods for performing operations
- Change an existing value
- Add a new value
- Remove a value
- Conclusion
Let's get started!
What are Python Arrays?
Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.
Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.
What's the Difference between Python Lists and Python Arrays?
Lists are one of the most common data structures in Python, and a core part of the language.
Lists and arrays behave similarly.
Just like arrays, lists are an ordered sequence of elements.
They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.
However, lists and arrays are not the same thing.
Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.
As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.
When to Use Python Arrays
Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.
Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.
They are also more compact and take up less memory and space which makes them more size efficient compared to lists.
If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.
How to Use Arrays in Python
In order to create Python arrays, you'll first have to import the array module which contains all the necassary functions.
There are three ways you can import the array module:
- By using
import arrayat the top of the file. This includes the modulearray. You would then go on to create an array usingarray.array().
import array
#how you would create an array
array.array()- Instead of having to type
array.array()all the time, you could useimport array as arrat the top of the file, instead ofimport arrayalone. You would then create an array by typingarr.array(). Thearracts as an alias name, with the array constructor then immediately following it.
import array as arr
#how you would create an array
arr.array()- Lastly, you could also use
from array import *, with*importing all the functionalities available. You would then create an array by writing thearray()constructor alone.
from array import *
#how you would create an array
array()How to Define Arrays in Python
Once you've imported the array module, you can then go on to define a Python array.
The general syntax for creating an array looks like this:
variable_name = array(typecode,[elements])Let's break it down:
variable_namewould be the name of the array.- The
typecodespecifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type. - Inside square brackets you mention the
elementsthat would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writingvariable_name = array(typecode)alone, without any elements.
Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:
| TYPECODE | C TYPE | PYTHON TYPE | SIZE |
|---|---|---|---|
| 'b' | signed char | int | 1 |
| 'B' | unsigned char | int | 1 |
| 'u' | wchar_t | Unicode character | 2 |
| 'h' | signed short | int | 2 |
| 'H' | unsigned short | int | 2 |
| 'i' | signed int | int | 2 |
| 'I' | unsigned int | int | 2 |
| 'l' | signed long | int | 4 |
| 'L' | unsigned long | int | 4 |
| 'q' | signed long long | int | 8 |
| 'Q' | unsigned long long | int | 8 |
| 'f' | float | float | 4 |
| 'd' | double | float | 8 |
Tying everything together, here is an example of how you would define an array in Python:
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers)
#output
#array('i', [10, 20, 30])Let's break it down:
- First we included the array module, in this case with
import array as arr. - Then, we created a
numbersarray. - We used
arr.array()because ofimport array as arr. - Inside the
array()constructor, we first includedi, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, withHfor example, would mean that no negative values are allowed. - Lastly, we included the values to be stored in the array in square brackets.
Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:
import array as arr
numbers = arr.array('i',[10.0,20,30])
print(numbers)
#output
#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
# numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integerIn the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.
Another way to create an array is the following:
from array import *
#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])
print(numbers)
#output
#array('d', [10.0, 20.0, 30.0])The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.
How to Find the Length of an Array in Python
To find out the exact number of elements contained in an array, use the built-in len() method.
It will return the integer number that is equal to the total number of elements in the array you specify.
import array as arr
numbers = arr.array('i',[10,20,30])
print(len(numbers))
#output
# 3In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3.
Array Indexing and How to Access Individual Items in an Array in Python
Each item in an array has a specific address. Individual items are accessed by referencing their index number.
Indexing in Python, and in all programming languages and computing in general, starts at 0. It is important to remember that counting starts at 0 and not at 1.
To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.
The general syntax would look something like this:
array_name[index_value_of_item]Here is how you would access each individual element in an array:
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element
#output
#10
#20
#30Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.
Note that you can also access each individual element using negative indexing.
With negative indexing, the last element would have an index of -1, the second to last element would have an index of -2, and so on.
Here is how you would get each item in an array using that method:
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item
#output
#30
#20
#10How to Search Through an Array in Python
You can find out an element's index number by using the index() method.
You pass the value of the element being searched as the argument to the method, and the element's index number is returned.
import array as arr
numbers = arr.array('i',[10,20,30])
#search for the index of the value 10
print(numbers.index(10))
#output
#0If there is more than one element with the same value, the index of the first instance of the value will be returned:
import array as arr
numbers = arr.array('i',[10,20,30,10,20,30])
#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))
#output
#0How to Loop through an Array in Python
You've seen how to access each individual element in an array and print it out on its own.
You've also seen how to print the array, using the print() method. That method gives the following result:
import array as arr
numbers = arr.array('i',[10,20,30])
print(numbers)
#output
#array('i', [10, 20, 30])What if you want to print each value one by one?
This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.
For this you can use a simple for loop:
import array as arr
numbers = arr.array('i',[10,20,30])
for number in numbers:
print(number)
#output
#10
#20
#30You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:
import array as arr
values = arr.array('i',[10,20,30])
#prints each individual value in the array
for value in range(len(values)):
print(values[value])
#output
#10
#20
#30How to Slice an Array in Python
To access a specific range of values inside the array, use the slicing operator, which is a colon :.
When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#get the values 10 and 20 only
print(numbers[:2]) #first to second position
#output
#array('i', [10, 20])When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#get the values 20 and 30 only
print(numbers[1:3]) #second to third position
#output
#rray('i', [20, 30])Methods For Performing Operations on Arrays in Python
Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.
Let's see some of the most commonly used methods which are used for performing operations on arrays.
How to Change the Value of an Item in an Array
You can change the value of a specific element by speficying its position and assigning it a new value:
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40
print(numbers)
#output
#array('i', [40, 20, 30])How to Add a New Value to an Array
To add one single value at the end of an array, use the append() method:
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 to the end of numbers
numbers.append(40)
print(numbers)
#output
#array('i', [10, 20, 30, 40])Be aware that the new item you add needs to be the same data type as the rest of the items in the array.
Look what happens when I try to add a float to an array of integers:
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 to the end of numbers
numbers.append(40.0)
print(numbers)
#output
#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
# numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integerBut what if you want to add more than one value to the end an array?
Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets
numbers.extend([40,50,60])
print(numbers)
#output
#array('i', [10, 20, 30, 40, 50, 60])And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.
The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
#add the integer 40 in the first position
#remember indexing starts at 0
numbers.insert(0,40)
print(numbers)
#output
#array('i', [40, 10, 20, 30])How to Remove a Value from an Array
To remove an element from an array, use the remove() method and include the value as an argument to the method.
import array as arr
#original array
numbers = arr.array('i',[10,20,30])
numbers.remove(10)
print(numbers)
#output
#array('i', [20, 30])With remove(), only the first instance of the value you pass as an argument will be removed.
See what happens when there are more than one identical values:
import array as arr
#original array
numbers = arr.array('i',[10,20,30,10,20])
numbers.remove(10)
print(numbers)
#output
#array('i', [20, 30, 10, 20])Only the first occurence of 10 is removed.
You can also use the pop() method, and specify the position of the element to be removed:
import array as arr
#original array
numbers = arr.array('i',[10,20,30,10,20])
#remove the first instance of 10
numbers.pop(0)
print(numbers)
#output
#array('i', [20, 30, 10, 20])Conclusion
And there you have it - you now know the basics of how to create arrays in Python using the array module. Hopefully you found this guide helpful.
Thanks for reading and happy coding!
#python #programming
1595494844
How to start an electric scooter facility/fleet in a university campus/IT park
Are you leading an organization that has a large campus, e.g., a large university? You are probably thinking of introducing an electric scooter/bicycle fleet on the campus, and why wouldn’t you?
Introducing micro-mobility in your campus with the help of such a fleet would help the people on the campus significantly. People would save money since they don’t need to use a car for a short distance. Your campus will see a drastic reduction in congestion, moreover, its carbon footprint will reduce.
Micro-mobility is relatively new though and you would need help. You would need to select an appropriate fleet of vehicles. The people on your campus would need to find electric scooters or electric bikes for commuting, and you need to provide a solution for this.
To be more specific, you need a short-term electric bike rental app. With such an app, you will be able to easily offer micro-mobility to the people on the campus. We at Devathon have built Autorent exactly for this.
What does Autorent do and how can it help you? How does it enable you to introduce micro-mobility on your campus? We explain these in this article, however, we will touch upon a few basics first.
Micro-mobility: What it is
You are probably thinking about micro-mobility relatively recently, aren’t you? A few relevant insights about it could help you to better appreciate its importance.
Micro-mobility is a new trend in transportation, and it uses vehicles that are considerably smaller than cars. Electric scooters (e-scooters) and electric bikes (e-bikes) are the most popular forms of micro-mobility, however, there are also e-unicycles and e-skateboards.
You might have already seen e-scooters, which are kick scooters that come with a motor. Thanks to its motor, an e-scooter can achieve a speed of up to 20 km/h. On the other hand, e-bikes are popular in China and Japan, and they come with a motor, and you can reach a speed of 40 km/h.
You obviously can’t use these vehicles for very long commutes, however, what if you need to travel a short distance? Even if you have a reasonable public transport facility in the city, it might not cover the route you need to take. Take the example of a large university campus. Such a campus is often at a considerable distance from the central business district of the city where it’s located. While public transport facilities may serve the central business district, they wouldn’t serve this large campus. Currently, many people drive their cars even for short distances.
As you know, that brings its own set of challenges. Vehicular traffic adds significantly to pollution, moreover, finding a parking spot can be hard in crowded urban districts.
Well, you can reduce your carbon footprint if you use an electric car. However, electric cars are still new, and many countries are still building the necessary infrastructure for them. Your large campus might not have the necessary infrastructure for them either. Presently, electric cars don’t represent a viable option in most geographies.
As a result, you need to buy and maintain a car even if your commute is short. In addition to dealing with parking problems, you need to spend significantly on your car.
All of these factors have combined to make people sit up and think seriously about cars. Many people are now seriously considering whether a car is really the best option even if they have to commute only a short distance.
This is where micro-mobility enters the picture. When you commute a short distance regularly, e-scooters or e-bikes are viable options. You limit your carbon footprints and you cut costs!
Businesses have seen this shift in thinking, and e-scooter companies like Lime and Bird have entered this field in a big way. They let you rent e-scooters by the minute. On the other hand, start-ups like Jump and Lyft have entered the e-bike market.
Think of your campus now! The people there might need to travel short distances within the campus, and e-scooters can really help them.
How micro-mobility can benefit you
What advantages can you get from micro-mobility? Let’s take a deeper look into this question.
Micro-mobility can offer several advantages to the people on your campus, e.g.:
- Affordability: Shared e-scooters are cheaper than other mass transportation options. Remember that the people on your campus will use them on a shared basis, and they will pay for their short commutes only. Well, depending on your operating model, you might even let them use shared e-scooters or e-bikes for free!
- Convenience: Users don’t need to worry about finding parking spots for shared e-scooters since these are small. They can easily travel from point A to point B on your campus with the help of these e-scooters.
- Environmentally sustainable: Shared e-scooters reduce the carbon footprint, moreover, they decongest the roads. Statistics from the pilot programs in cities like Portland and Denver showimpressive gains around this key aspect.
- Safety: This one’s obvious, isn’t it? When people on your campus use small e-scooters or e-bikes instead of cars, the problem of overspeeding will disappear. you will see fewer accidents.
#android app #autorent #ios app #mobile app development #app like bird #app like bounce #app like lime #autorent #bird scooter business model #bird scooter rental #bird scooter rental cost #bird scooter rental price #clone app like bird #clone app like bounce #clone app like lime #electric rental scooters #electric scooter company #electric scooter rental business #how do you start a moped #how to start a moped #how to start a scooter rental business #how to start an electric company #how to start electric scooterrental business #lime scooter business model #scooter franchise #scooter rental business #scooter rental business for sale #scooter rental business insurance #scooters franchise cost #white label app like bird #white label app like bounce #white label app like lime
1595491178
Best Electric Bikes and Scooters for Rental Business or Campus Facility
The electric scooter revolution has caught on super-fast taking many cities across the globe by storm. eScooters, a renovated version of old-school scooters now turned into electric vehicles are an environmentally friendly solution to current on-demand commute problems. They work on engines, like cars, enabling short traveling distances without hassle. The result is that these groundbreaking electric machines can now provide faster transport for less — cheaper than Uber and faster than Metro.
Since they are durable, fast, easy to operate and maintain, and are more convenient to park compared to four-wheelers, the eScooters trend has and continues to spike interest as a promising growth area. Several companies and universities are increasingly setting up shop to provide eScooter services realizing a would-be profitable business model and a ready customer base that is university students or residents in need of faster and cheap travel going about their business in school, town, and other surrounding areas.
Electric Scooters Trends and Statistics
In many countries including the U.S., Canada, Mexico, U.K., Germany, France, China, Japan, India, Brazil and Mexico and more, a growing number of eScooter users both locals and tourists can now be seen effortlessly passing lines of drivers stuck in the endless and unmoving traffic.
A recent report by McKinsey revealed that the E-Scooter industry will be worth― $200 billion to $300 billion in the United States, $100 billion to $150 billion in Europe, and $30 billion to $50 billion in China in 2030. The e-Scooter revenue model will also spike and is projected to rise by more than 20% amounting to approximately $5 billion.
And, with a necessity to move people away from high carbon prints, traffic and congestion issues brought about by car-centric transport systems in cities, more and more city planners are developing more bike/scooter lanes and adopting zero-emission plans. This is the force behind the booming electric scooter market and the numbers will only go higher and higher.
Companies that have taken advantage of the growing eScooter trend develop an appthat allows them to provide efficient eScooter services. Such an app enables them to be able to locate bike pick-up and drop points through fully integrated google maps.
List of Best Electric Bikes for Rental Business or Campus Facility 2020:
It’s clear that e scooters will increasingly become more common and the e-scooter business model will continue to grab the attention of manufacturers, investors, entrepreneurs. All this should go ahead with a quest to know what are some of the best electric bikes in the market especially for anyone who would want to get started in the electric bikes/scooters rental business.
We have done a comprehensive list of the best electric bikes! Each bike has been reviewed in depth and includes a full list of specs and a photo.
Billy eBike
https://www.kickstarter.com/projects/enkicycles/billy-were-redefining-joyrides
To start us off is the Billy eBike, a powerful go-anywhere urban electric bike that’s specially designed to offer an exciting ride like no other whether you want to ride to the grocery store, cafe, work or school. The Billy eBike comes in 4 color options – Billy Blue, Polished aluminium, Artic white, and Stealth black.
Price: $2490
Available countries
Available in the USA, Europe, Asia, South Africa and Australia.This item ships from the USA. Buyers are therefore responsible for any taxes and/or customs duties incurred once it arrives in your country.
Features
- Control – Ride with confidence with our ultra-wide BMX bars and a hyper-responsive twist throttle.
- Stealth- Ride like a ninja with our Gates carbon drive that’s as smooth as butter and maintenance-free.
- Drive – Ride further with our high torque fat bike motor, giving a better climbing performance.
- Accelerate – Ride quicker with our 20-inch lightweight cutout rims for improved acceleration.
- Customize – Ride your own way with 5 levels of power control. Each level determines power and speed.
- Flickable – Ride harder with our BMX /MotoX inspired geometry and lightweight aluminum package
Specifications
- Maximum speed: 20 mph (32 km/h)
- Range per charge: 41 miles (66 km)
- Maximum Power: 500W
- Motor type: Fat Bike Motor: Bafang RM G060.500.DC
- Load capacity: 300lbs (136kg)
- Battery type: 13.6Ah Samsung lithium-ion,
- Battery capacity: On/off-bike charging available
- Weight: w/o batt. 48.5lbs (22kg), w/ batt. 54lbs (24.5kg)
- Front Suspension: Fully adjustable air shock, preload/compression damping /lockout
- Rear Suspension: spring, preload adjustment
- Built-in GPS
Why Should You Buy This?
- Riding fun and excitement
- Better climbing ability and faster acceleration.
- Ride with confidence
- Billy folds for convenient storage and transportation.
- Shorty levers connect to disc brakes ensuring you stop on a dime
- belt drives are maintenance-free and clean (no oil or lubrication needed)
**Who Should Ride Billy? **
Both new and experienced riders
**Where to Buy? **Local distributors or ships from the USA.
Genze 200 series e-Bike
Featuring a sleek and lightweight aluminum frame design, the 200-Series ebike takes your riding experience to greater heights. Available in both black and white this ebike comes with a connected app, which allows you to plan activities, map distances and routes while also allowing connections with fellow riders.
Price: $2099.00
Available countries
The Genze 200 series e-Bike is available at GenZe retail locations across the U.S or online via GenZe.com website. Customers from outside the US can ship the product while incurring the relevant charges.
Features
- 2 Frame Options
- 2 Sizes
- Integrated/Removable Battery
- Throttle and Pedal Assist Ride Modes
- Integrated LCD Display
- Connected App
- 24 month warranty
- GPS navigation
- Bluetooth connectivity
Specifications
- Maximum speed: 20 mph with throttle
- Range per charge: 15-18 miles w/ throttle and 30-50 miles w/ pedal assist
- Charging time: 3.5 hours
- Motor type: Brushless Rear Hub Motor
- Gears: Microshift Thumb Shifter
- Battery type: Removable Samsung 36V, 9.6AH Li-Ion battery pack
- Battery capacity: 36V and 350 Wh
- Weight: 46 pounds
- Derailleur: 8-speed Shimano
- Brakes: Dual classic
- Wheels: 26 x 20 inches
- Frame: 16, and 18 inches
- Operating Mode: Analog mode 5 levels of Pedal Assist Throttle Mode
Norco from eBikestore
https://ebikestore.com/shop/norco-vlt-s2/
The Norco VLT S2 is a front suspension e-Bike with solid components alongside the reliable Bosch Performance Line Power systems that offer precise pedal assistance during any riding situation.
Price: $2,699.00
Available countries
This item is available via the various Norco bikes international distributors.
Features
- VLT aluminum frame- for stiffness and wheel security.
- Bosch e-bike system – for their reliability and performance.
- E-bike components – for added durability.
- Hydraulic disc brakes – offer riders more stopping power for safety and control at higher speeds.
- Practical design features – to add convenience and versatility.
Specifications
- Maximum speed: KMC X9 9spd
- Motor type: Bosch Active Line
- Gears: Shimano Altus RD-M2000, SGS, 9 Speed
- Battery type: Power Pack 400
- Battery capacity: 396Wh
- Suspension: SR Suntour suspension fork
- Frame: Norco VLT, Aluminum, 12x142mm TA Dropouts
Bodo EV
http://www.bodoevs.com/bodoev/products_show.asp?product_id=13
Manufactured by Bodo Vehicle Group Limited, the Bodo EV is specially designed for strong power and extraordinary long service to facilitate super amazing rides. The Bodo Vehicle Company is a striking top in electric vehicles brand field in China and across the globe. Their Bodo EV will no doubt provide your riders with high-level riding satisfaction owing to its high-quality design, strength, breaking stability and speed.
Price: $799
Available countries
This item ships from China with buyers bearing the shipping costs and other variables prior to delivery.
Features
- Reliable
- Environment friendly
- Comfortable riding
- Fashionable
- Economical
- Durable – long service life
- Braking stability
- LED lighting technology
Specifications
- Maximum speed: 45km/h
- Range per charge: 50km per person
- Charging time: 8 hours
- Maximum Power: 3000W
- Motor type: Brushless DC Motor
- Load capacity: 100kg
- Battery type: Lead-acid battery
- Battery capacity: 60V 20AH
- Weight: w/o battery 47kg
#android app #autorent #entrepreneurship #ios app #minimum viable product (mvp) #mobile app development #news #app like bird #app like bounce #app like lime #autorent #best electric bikes 2020 #best electric bikes for rental business #best electric kick scooters 2020 #best electric kickscooters for rental business #best electric scooters 2020 #best electric scooters for rental business #bird scooter business model #bird scooter rental #bird scooter rental cost #bird scooter rental price #clone app like bird #clone app like bounce #clone app like lime #electric rental scooters #electric scooter company #electric scooter rental business #how do you start a moped #how to start a moped #how to start a scooter rental business #how to start an electric company #how to start electric scooterrental business #lime scooter business model #scooter franchise #scooter rental business #scooter rental business for sale #scooter rental business insurance #scooters franchise cost #white label app like bird #white label app like bounce #white label app like lime
1667468640
Perl Critic: The Leading Static analyzer for Perl
BUILD STATUS
NAME
Perl::Critic - Critique Perl source code for best-practices.
SYNOPSIS
use Perl::Critic;
my $file = shift;
my $critic = Perl::Critic->new();
my @violations = $critic->critique($file);
print @violations;
DESCRIPTION
Perl::Critic is an extensible framework for creating and applying coding standards to Perl source code. Essentially, it is a static source code analysis engine. Perl::Critic is distributed with a number of Perl::Critic::Policy modules that attempt to enforce various coding guidelines. Most Policy modules are based on Damian Conway's book Perl Best Practices. However, Perl::Critic is not limited to PBP and will even support Policies that contradict Conway. You can enable, disable, and customize those Polices through the Perl::Critic interface. You can also create new Policy modules that suit your own tastes.
For a command-line interface to Perl::Critic, see the documentation for perlcritic. If you want to integrate Perl::Critic with your build process, Test::Perl::Critic provides an interface that is suitable for test programs. Also, Test::Perl::Critic::Progressive is useful for gradually applying coding standards to legacy code. For the ultimate convenience (at the expense of some flexibility) see the criticism pragma.
If you'd like to try Perl::Critic without installing anything, there is a web-service available at http://perlcritic.com. The web-service does not yet support all the configuration features that are available in the native Perl::Critic API, but it should give you a good idea of what it does.
Also, ActivePerl includes a very slick graphical interface to Perl-Critic called perlcritic-gui. You can get a free community edition of ActivePerl from http://www.activestate.com.
PREREQUISITES
Perl::Critic runs on Perl back to Perl 5.6.1. It relies on the PPI module to do the heavy work of parsing Perl.
INTERFACE SUPPORT
The Perl::Critic module is considered to be a public class. Any changes to its interface will go through a deprecation cycle.
CONSTRUCTOR
new( [ -profile => $FILE, -severity => $N, -theme => $string, -include => \@PATTERNS, -exclude => \@PATTERNS, -top => $N, -only => $B, -profile-strictness => $PROFILE_STRICTNESS_{WARN|FATAL|QUIET}, -force => $B, -verbose => $N ], -color => $B, -pager => $string, -allow-unsafe => $B, -criticism-fatal => $B)
new()
Returns a reference to a new Perl::Critic object. Most arguments are just passed directly into Perl::Critic::Config, but I have described them here as well. The default value for all arguments can be defined in your .perlcriticrc file. See the "CONFIGURATION" section for more information about that. All arguments are optional key-value pairs as follows:
-profile is a path to a configuration file. If $FILE is not defined, Perl::Critic::Config attempts to find a .perlcriticrc configuration file in the current directory, and then in your home directory. Alternatively, you can set the PERLCRITIC environment variable to point to a file in another location. If a configuration file can't be found, or if $FILE is an empty string, then all Policies will be loaded with their default configuration. See "CONFIGURATION" for more information.
-severity is the minimum severity level. Only Policy modules that have a severity greater than $N will be applied. Severity values are integers ranging from 1 (least severe violations) to 5 (most severe violations). The default is 5. For a given -profile, decreasing the -severity will usually reveal more Policy violations. You can set the default value for this option in your .perlcriticrc file. Users can redefine the severity level for any Policy in their .perlcriticrc file. See "CONFIGURATION" for more information.
If it is difficult for you to remember whether severity "5" is the most or least restrictive level, then you can use one of these named values:
SEVERITY NAME ...is equivalent to... SEVERITY NUMBER
--------------------------------------------------------
-severity => 'gentle' -severity => 5
-severity => 'stern' -severity => 4
-severity => 'harsh' -severity => 3
-severity => 'cruel' -severity => 2
-severity => 'brutal' -severity => 1
The names reflect how severely the code is criticized: a gentle criticism reports only the most severe violations, and so on down to a brutal criticism which reports even the most minor violations.
-theme is special expression that determines which Policies to apply based on their respective themes. For example, the following would load only Policies that have a 'bugs' AND 'pbp' theme:
my $critic = Perl::Critic->new( -theme => 'bugs && pbp' );
Unless the -severity option is explicitly given, setting -theme silently causes the -severity to be set to 1. You can set the default value for this option in your .perlcriticrc file. See the "POLICY THEMES" section for more information about themes.
-include is a reference to a list of string @PATTERNS. Policy modules that match at least one m/$PATTERN/ixms will always be loaded, irrespective of all other settings. For example:
my $critic = Perl::Critic->new(-include => ['layout'], -severity => 4);
This would cause Perl::Critic to apply all the CodeLayout::* Policy modules even though they have a severity level that is less than 4. You can set the default value for this option in your .perlcriticrc file. You can also use -include in conjunction with the -exclude option. Note that -exclude takes precedence over -include when a Policy matches both patterns.
-exclude is a reference to a list of string @PATTERNS. Policy modules that match at least one m/$PATTERN/ixms will not be loaded, irrespective of all other settings. For example:
my $critic = Perl::Critic->new(-exclude => ['strict'], -severity => 1);
This would cause Perl::Critic to not apply the RequireUseStrict and ProhibitNoStrict Policy modules even though they have a severity level that is greater than 1. You can set the default value for this option in your .perlcriticrc file. You can also use -exclude in conjunction with the -include option. Note that -exclude takes precedence over -include when a Policy matches both patterns.
-single-policy is a string PATTERN. Only one policy that matches m/$PATTERN/ixms will be used. Policies that do not match will be excluded. This option has precedence over the -severity, -theme, -include, -exclude, and -only options. You can set the default value for this option in your .perlcriticrc file.
-top is the maximum number of Violations to return when ranked by their severity levels. This must be a positive integer. Violations are still returned in the order that they occur within the file. Unless the -severity option is explicitly given, setting -top silently causes the -severity to be set to 1. You can set the default value for this option in your .perlcriticrc file.
-only is a boolean value. If set to a true value, Perl::Critic will only choose from Policies that are mentioned in the user's profile. If set to a false value (which is the default), then Perl::Critic chooses from all the Policies that it finds at your site. You can set the default value for this option in your .perlcriticrc file.
-profile-strictness is an enumerated value, one of "$PROFILE_STRICTNESS_WARN" in Perl::Critic::Utils::Constants (the default), "$PROFILE_STRICTNESS_FATAL" in Perl::Critic::Utils::Constants, and "$PROFILE_STRICTNESS_QUIET" in Perl::Critic::Utils::Constants. If set to "$PROFILE_STRICTNESS_FATAL" in Perl::Critic::Utils::Constants, Perl::Critic will make certain warnings about problems found in a .perlcriticrc or file specified via the -profile option fatal. For example, Perl::Critic normally only warns about profiles referring to non-existent Policies, but this value makes this situation fatal. Correspondingly, "$PROFILE_STRICTNESS_QUIET" in Perl::Critic::Utils::Constants makes Perl::Critic shut up about these things.
-force is a boolean value that controls whether Perl::Critic observes the magical "## no critic" annotations in your code. If set to a true value, Perl::Critic will analyze all code. If set to a false value (which is the default) Perl::Critic will ignore code that is tagged with these annotations. See "BENDING THE RULES" for more information. You can set the default value for this option in your .perlcriticrc file.
-verbose can be a positive integer (from 1 to 11), or a literal format specification. See Perl::Critic::Violation for an explanation of format specifications. You can set the default value for this option in your .perlcriticrc file.
-unsafe directs Perl::Critic to allow the use of Policies that are marked as "unsafe" by the author. Such policies may compile untrusted code or do other nefarious things.
-color and -pager are not used by Perl::Critic but is provided for the benefit of perlcritic.
-criticism-fatal is not used by Perl::Critic but is provided for the benefit of criticism.
-color-severity-highest, -color-severity-high, -color-severity- medium, -color-severity-low, and -color-severity-lowest are not used by Perl::Critic, but are provided for the benefit of perlcritic. Each is set to the Term::ANSIColor color specification to be used to display violations of the corresponding severity.
-files-with-violations and -files-without-violations are not used by Perl::Critic, but are provided for the benefit of perlcritic, to cause only the relevant filenames to be displayed.
METHODS
critique( $source_code )
Runs the $source_code through the Perl::Critic engine using all the Policies that have been loaded into this engine. If $source_code is a scalar reference, then it is treated as a string of actual Perl code. If $source_code is a reference to an instance of PPI::Document, then that instance is used directly. Otherwise, it is treated as a path to a local file containing Perl code. This method returns a list of Perl::Critic::Violation objects for each violation of the loaded Policies. The list is sorted in the order that the Violations appear in the code. If there are no violations, this method returns an empty list.
add_policy( -policy => $policy_name, -params => \%param_hash )
Creates a Policy object and loads it into this Critic. If the object cannot be instantiated, it will throw a fatal exception. Otherwise, it returns a reference to this Critic.
-policy is the name of a Perl::Critic::Policy subclass module. The 'Perl::Critic::Policy' portion of the name can be omitted for brevity. This argument is required.
-params is an optional reference to a hash of Policy parameters. The contents of this hash reference will be passed into to the constructor of the Policy module. See the documentation in the relevant Policy module for a description of the arguments it supports.
policies()
Returns a list containing references to all the Policy objects that have been loaded into this engine. Objects will be in the order that they were loaded.
config()
Returns the Perl::Critic::Config object that was created for or given to this Critic.
statistics()
Returns the Perl::Critic::Statistics object that was created for this Critic. The Statistics object accumulates data for all files that are analyzed by this Critic.
FUNCTIONAL INTERFACE
For those folks who prefer to have a functional interface, The critique method can be exported on request and called as a static function. If the first argument is a hashref, its contents are used to construct a new Perl::Critic object internally. The keys of that hash should be the same as those supported by the Perl::Critic::new() method. Here are some examples:
use Perl::Critic qw(critique);
# Use default parameters...
@violations = critique( $some_file );
# Use custom parameters...
@violations = critique( {-severity => 2}, $some_file );
# As a one-liner
%> perl -MPerl::Critic=critique -e 'print critique(shift)' some_file.pm
None of the other object-methods are currently supported as static functions. Sorry.
CONFIGURATION
Most of the settings for Perl::Critic and each of the Policy modules can be controlled by a configuration file. The default configuration file is called .perlcriticrc. Perl::Critic will look for this file in the current directory first, and then in your home directory. Alternatively, you can set the PERLCRITIC environment variable to explicitly point to a different file in another location. If none of these files exist, and the -profile option is not given to the constructor, then all the modules that are found in the Perl::Critic::Policy namespace will be loaded with their default configuration.
The format of the configuration file is a series of INI-style blocks that contain key-value pairs separated by '='. Comments should start with '#' and can be placed on a separate line or after the name-value pairs if you desire.
Default settings for Perl::Critic itself can be set before the first named block. For example, putting any or all of these at the top of your configuration file will set the default value for the corresponding constructor argument.
severity = 3 #Integer or named level
only = 1 #Zero or One
force = 0 #Zero or One
verbose = 4 #Integer or format spec
top = 50 #A positive integer
theme = (pbp || security) && bugs #A theme expression
include = NamingConventions ClassHierarchies #Space-delimited list
exclude = Variables Modules::RequirePackage #Space-delimited list
criticism-fatal = 1 #Zero or One
color = 1 #Zero or One
allow-unsafe = 1 #Zero or One
pager = less #pager to pipe output to
The remainder of the configuration file is a series of blocks like this:
[Perl::Critic::Policy::Category::PolicyName]
severity = 1
set_themes = foo bar
add_themes = baz
maximum_violations_per_document = 57
arg1 = value1
arg2 = value2
Perl::Critic::Policy::Category::PolicyName is the full name of a module that implements the policy. The Policy modules distributed with Perl::Critic have been grouped into categories according to the table of contents in Damian Conway's book Perl Best Practices. For brevity, you can omit the 'Perl::Critic::Policy' part of the module name.
severity is the level of importance you wish to assign to the Policy. All Policy modules are defined with a default severity value ranging from 1 (least severe) to 5 (most severe). However, you may disagree with the default severity and choose to give it a higher or lower severity, based on your own coding philosophy. You can set the severity to an integer from 1 to 5, or use one of the equivalent names:
SEVERITY NAME ...is equivalent to... SEVERITY NUMBER
----------------------------------------------------
gentle 5
stern 4
harsh 3
cruel 2
brutal 1
The names reflect how severely the code is criticized: a gentle criticism reports only the most severe violations, and so on down to a brutal criticism which reports even the most minor violations.
set_themes sets the theme for the Policy and overrides its default theme. The argument is a string of one or more whitespace-delimited alphanumeric words. Themes are case-insensitive. See "POLICY THEMES" for more information.
add_themes appends to the default themes for this Policy. The argument is a string of one or more whitespace-delimited words. Themes are case- insensitive. See "POLICY THEMES" for more information.
maximum_violations_per_document limits the number of Violations the Policy will return for a given document. Some Policies have a default limit; see the documentation for the individual Policies to see whether there is one. To force a Policy to not have a limit, specify "no_limit" or the empty string for the value of this parameter.
The remaining key-value pairs are configuration parameters that will be passed into the constructor for that Policy. The constructors for most Policy objects do not support arguments, and those that do should have reasonable defaults. See the documentation on the appropriate Policy module for more details.
Instead of redefining the severity for a given Policy, you can completely disable a Policy by prepending a '-' to the name of the module in your configuration file. In this manner, the Policy will never be loaded, regardless of the -severity given to the Perl::Critic constructor.
A simple configuration might look like this:
#--------------------------------------------------------------
# I think these are really important, so always load them
[TestingAndDebugging::RequireUseStrict]
severity = 5
[TestingAndDebugging::RequireUseWarnings]
severity = 5
#--------------------------------------------------------------
# I think these are less important, so only load when asked
[Variables::ProhibitPackageVars]
severity = 2
[ControlStructures::ProhibitPostfixControls]
allow = if unless # My custom configuration
severity = cruel # Same as "severity = 2"
#--------------------------------------------------------------
# Give these policies a custom theme. I can activate just
# these policies by saying `perlcritic -theme larry`
[Modules::RequireFilenameMatchesPackage]
add_themes = larry
[TestingAndDebugging::RequireTestLables]
add_themes = larry curly moe
#--------------------------------------------------------------
# I do not agree with these at all, so never load them
[-NamingConventions::Capitalization]
[-ValuesAndExpressions::ProhibitMagicNumbers]
#--------------------------------------------------------------
# For all other Policies, I accept the default severity,
# so no additional configuration is required for them.
For additional configuration examples, see the perlcriticrc file that is included in this examples directory of this distribution.
Damian Conway's own Perl::Critic configuration is also included in this distribution as examples/perlcriticrc-conway.
THE POLICIES
A large number of Policy modules are distributed with Perl::Critic. They are described briefly in the companion document Perl::Critic::PolicySummary and in more detail in the individual modules themselves. Say "perlcritic -doc PATTERN" to see the perldoc for all Policy modules that match the regex m/PATTERN/ixms
There are a number of distributions of additional policies on CPAN. If Perl::Critic doesn't contain a policy that you want, some one may have already written it. See the "SEE ALSO" section below for a list of some of these distributions.
POLICY THEMES
Each Policy is defined with one or more "themes". Themes can be used to create arbitrary groups of Policies. They are intended to provide an alternative mechanism for selecting your preferred set of Policies. For example, you may wish disable a certain subset of Policies when analyzing test programs. Conversely, you may wish to enable only a specific subset of Policies when analyzing modules.
The Policies that ship with Perl::Critic have been broken into the following themes. This is just our attempt to provide some basic logical groupings. You are free to invent new themes that suit your needs.
THEME DESCRIPTION
--------------------------------------------------------------------------
core All policies that ship with Perl::Critic
pbp Policies that come directly from "Perl Best Practices"
bugs Policies that that prevent or reveal bugs
certrec Policies that CERT recommends
certrule Policies that CERT considers rules
maintenance Policies that affect the long-term health of the code
cosmetic Policies that only have a superficial effect
complexity Policies that specifically relate to code complexity
security Policies that relate to security issues
tests Policies that are specific to test programs
Any Policy may fit into multiple themes. Say "perlcritic -list" to get a listing of all available Policies and the themes that are associated with each one. You can also change the theme for any Policy in your .perlcriticrc file. See the "CONFIGURATION" section for more information about that.
Using the -theme option, you can create an arbitrarily complex rule that determines which Policies will be loaded. Precedence is the same as regular Perl code, and you can use parentheses to enforce precedence as well. Supported operators are:
Operator Alternative Example
-----------------------------------------------------------------
&& and 'pbp && core'
|| or 'pbp || (bugs && security)'
! not 'pbp && ! (portability || complexity)'
Theme names are case-insensitive. If the -theme is set to an empty string, then it evaluates as true all Policies.
BENDING THE RULES
Perl::Critic takes a hard-line approach to your code: either you comply or you don't. In the real world, it is not always practical (nor even possible) to fully comply with coding standards. In such cases, it is wise to show that you are knowingly violating the standards and that you have a Damn Good Reason (DGR) for doing so.
To help with those situations, you can direct Perl::Critic to ignore certain lines or blocks of code by using annotations:
require 'LegacyLibaray1.pl'; ## no critic
require 'LegacyLibrary2.pl'; ## no critic
for my $element (@list) {
## no critic
$foo = ""; #Violates 'ProhibitEmptyQuotes'
$barf = bar() if $foo; #Violates 'ProhibitPostfixControls'
#Some more evil code...
## use critic
#Some good code...
do_something($_);
}
The "## no critic" annotations direct Perl::Critic to ignore the remaining lines of code until a "## use critic" annotation is found. If the "## no critic" annotation is on the same line as a code statement, then only that line of code is overlooked. To direct perlcritic to ignore the "## no critic" annotations, use the --force option.
A bare "## no critic" annotation disables all the active Policies. If you wish to disable only specific Policies, add a list of Policy names as arguments, just as you would for the "no strict" or "no warnings" pragmas. For example, this would disable the ProhibitEmptyQuotes and ProhibitPostfixControls policies until the end of the block or until the next "## use critic" annotation (whichever comes first):
## no critic (EmptyQuotes, PostfixControls)
# Now exempt from ValuesAndExpressions::ProhibitEmptyQuotes
$foo = "";
# Now exempt ControlStructures::ProhibitPostfixControls
$barf = bar() if $foo;
# Still subjected to ValuesAndExpression::RequireNumberSeparators
$long_int = 10000000000;
Since the Policy names are matched against the "## no critic" arguments as regular expressions, you can abbreviate the Policy names or disable an entire family of Policies in one shot like this:
## no critic (NamingConventions)
# Now exempt from NamingConventions::Capitalization
my $camelHumpVar = 'foo';
# Now exempt from NamingConventions::Capitalization
sub camelHumpSub {}
The argument list must be enclosed in parentheses or brackets and must contain one or more comma-separated barewords (e.g. don't use quotes). The "## no critic" annotations can be nested, and Policies named by an inner annotation will be disabled along with those already disabled an outer annotation.
Some Policies like Subroutines::ProhibitExcessComplexity apply to an entire block of code. In those cases, the "## no critic" annotation must appear on the line where the violation is reported. For example:
sub complicated_function { ## no critic (ProhibitExcessComplexity)
# Your code here...
}
Policies such as Documentation::RequirePodSections apply to the entire document, in which case violations are reported at line 1.
Use this feature wisely. "## no critic" annotations should be used in the smallest possible scope, or only on individual lines of code. And you should always be as specific as possible about which Policies you want to disable (i.e. never use a bare "## no critic"). If Perl::Critic complains about your code, try and find a compliant solution before resorting to this feature.
THE Perl::Critic PHILOSOPHY
Coding standards are deeply personal and highly subjective. The goal of Perl::Critic is to help you write code that conforms with a set of best practices. Our primary goal is not to dictate what those practices are, but rather, to implement the practices discovered by others. Ultimately, you make the rules -- Perl::Critic is merely a tool for encouraging consistency. If there is a policy that you think is important or that we have overlooked, we would be very grateful for contributions, or you can simply load your own private set of policies into Perl::Critic.
EXTENDING THE CRITIC
The modular design of Perl::Critic is intended to facilitate the addition of new Policies. You'll need to have some understanding of PPI, but most Policy modules are pretty straightforward and only require about 20 lines of code. Please see the Perl::Critic::DEVELOPER file included in this distribution for a step-by-step demonstration of how to create new Policy modules.
If you develop any new Policy modules, feel free to send them to <team@perlcritic.com> and I'll be happy to consider putting them into the Perl::Critic distribution. Or if you would like to work on the Perl::Critic project directly, you can fork our repository at https://github.com/Perl-Critic/Perl-Critic.git.
The Perl::Critic team is also available for hire. If your organization has its own coding standards, we can create custom Policies to enforce your local guidelines. Or if your code base is prone to a particular defect pattern, we can design Policies that will help you catch those costly defects before they go into production. To discuss your needs with the Perl::Critic team, just contact <team@perlcritic.com>.
PREREQUISITES
Perl::Critic requires the following modules:
CONTACTING THE DEVELOPMENT TEAM
You are encouraged to subscribe to the public mailing list at https://groups.google.com/d/forum/perl-critic. At least one member of the development team is usually hanging around in irc://irc.perl.org/#perlcritic and you can follow Perl::Critic on Twitter, at https://twitter.com/perlcritic.
SEE ALSO
There are a number of distributions of additional Policies available. A few are listed here:
These distributions enable you to use Perl::Critic in your unit tests:
Test::Perl::Critic::Progressive
There is also a distribution that will install all the Perl::Critic related modules known to the development team:
BUGS
Scrutinizing Perl code is hard for humans, let alone machines. If you find any bugs, particularly false-positives or false-negatives from a Perl::Critic::Policy, please submit them at https://github.com/Perl-Critic/Perl-Critic/issues. Thanks.
CREDITS
Adam Kennedy - For creating PPI, the heart and soul of Perl::Critic.
Damian Conway - For writing Perl Best Practices, finally :)
Chris Dolan - For contributing the best features and Policy modules.
Andy Lester - Wise sage and master of all-things-testing.
Elliot Shank - The self-proclaimed quality freak.
Giuseppe Maxia - For all the great ideas and positive encouragement.
and Sharon, my wife - For putting up with my all-night code sessions.
Thanks also to the Perl Foundation for providing a grant to support Chris Dolan's project to implement twenty PBP policies. http://www.perlfoundation.org/april_1_2007_new_grant_awards
Thanks also to this incomplete laundry list of folks who have contributed to Perl::Critic in some way: Gregory Oschwald, Mike O'Regan, Tom Hukins, Omer Gazit, Evan Zacks, Paul Howarth, Sawyer X, Christian Walde, Dave Rolsky, Jakub Wilk, Roy Ivy III, Oliver Trosien, Glenn Fowler, Matt Creenan, Alex Balhatchet, Sebastian Paaske Tørholm, Stuart A Johnston, Dan Book, Steven Humphrey, James Raspass, Nick Tonkin, Harrison Katz, Douglas Sims, Mark Fowler, Alan Berndt, Neil Bowers, Sergey Romanov, Gabor Szabo, Graham Knop, Mike Eldridge, David Steinbrunner, Kirk Kimmel, Guillaume Aubert, Dave Cross, Anirvan Chatterjee, Todd Rinaldo, Graham Ollis, Karen Etheridge, Jonas Brømsø, Olaf Alders, Jim Keenan, Slaven Rezić, Szymon Nieznański.
AUTHOR
Jeffrey Ryan Thalhammer jeff@imaginative-software.com
COPYRIGHT
Copyright (c) 2005-2018 Imaginative Software Systems. All rights reserved.
This program is free software; you can redistribute it and/or modify it under the same terms as Perl itself. The full text of this license can be found in the LICENSE file included with this module.
Download Details:
Author: Perl-Critic
Source Code: https://github.com/Perl-Critic/Perl-Critic
License: View license