How to Find Permutations of a Given Array using Backtracking Algorithm

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How to Find Permutations of a Given Array using Backtracking Algorithm

Connor Mills

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.

Introduction to Arrays
1. The differences between Lists and Arrays
2. When to use arrays
How to use arrays
1. Define arrays
2. Find the length of arrays
3. Array indexing
4. Search through arrays
5. Loop through arrays
6. Slice an array
Array methods for performing operations
1. Change an existing value
2. Add a new value
3. 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 array at the top of the file. This includes the module array. You would then go on to create an array using array.array().

import array

#how you would create an array
array.array()

Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts 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 the array() 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_name would be the name of the array.
The typecode specifies 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 elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_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 numbers array.
We used arr.array() because of import array as arr .
Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for 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

Alex O'Sullivan

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.

Introduction to Arrays
1. The differences between Lists and Arrays
2. When to use arrays
How to use arrays
1. Define arrays
2. Find the length of arrays
3. Array indexing
4. Search through arrays
5. Loop through arrays
6. Slice an array
Array methods for performing operations
1. Change an existing value
2. Add a new value
3. 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.

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.

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.

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 array at the top of the file. This includes the module array. You would then go on to create an array using array.array().

import array

#how you would create an array
array.array()

Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array(). The arr acts 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 the array() 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_name would be the name of the array.
The typecode specifies 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 elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_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 numbers array.
We used arr.array() because of import array as arr .
Inside the array() constructor, we first included i, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for 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])

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.

Thanks for reading and happy coding!

#python #programming

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

Design the PCB using your favorite CAD or drawing software.
Print the top and bottom copper and top silk screen layers to a PDF file.
Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
Use a Gerber viewer to double-check the output against the original PCB design.
Make adjustments as needed.
Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl

Anissa Barrows

1669099573

What Is Face Recognition? Facial Recognition with Python and OpenCV

In this article, we will know what is face recognition and how is different from face detection. We will go briefly over the theory of face recognition and then jump on to the coding section. At the end of this article, you will be able to make a face recognition program for recognizing faces in images as well as on a live webcam feed.

What is Face Detection?

In computer vision, one essential problem we are trying to figure out is to automatically detect objects in an image without human intervention. Face detection can be thought of as such a problem where we detect human faces in an image. There may be slight differences in the faces of humans but overall, it is safe to say that there are certain features that are associated with all the human faces. There are various face detection algorithms but Viola-Jones Algorithm is one of the oldest methods that is also used today and we will use the same later in the article. You can go through the Viola-Jones Algorithm after completing this article as I’ll link it at the end of this article.

Face detection is usually the first step towards many face-related technologies, such as face recognition or verification. However, face detection can have very useful applications. The most successful application of face detection would probably be photo taking. When you take a photo of your friends, the face detection algorithm built into your digital camera detects where the faces are and adjusts the focus accordingly.

For a tutorial on Real-Time Face detection

What is Face Recognition?

face recognition

Now that we are successful in making such algorithms that can detect faces, can we also recognise whose faces are they?

Face recognition is a method of identifying or verifying the identity of an individual using their face. There are various algorithms that can do face recognition but their accuracy might vary. Here I am going to describe how we do face recognition using deep learning.

So now let us understand how we recognise faces using deep learning. We make use of face embedding in which each face is converted into a vector and this technique is called deep metric learning. Let me further divide this process into three simple steps for easy understanding:

Face Detection: The very first task we perform is detecting faces in the image or video stream. Now that we know the exact location/coordinates of face, we extract this face for further processing ahead.

Feature Extraction: Now that we have cropped the face out of the image, we extract features from it. Here we are going to use face embeddings to extract the features out of the face. A neural network takes an image of the person’s face as input and outputs a vector which represents the most important features of a face. In machine learning, this vector is called embedding and thus we call this vector as face embedding. Now how does this help in recognizing faces of different persons?

While training the neural network, the network learns to output similar vectors for faces that look similar. For example, if I have multiple images of faces within different timespan, of course, some of the features of my face might change but not up to much extent. So in this case the vectors associated with the faces are similar or in short, they are very close in the vector space. Take a look at the below diagram for a rough idea:

Now after training the network, the network learns to output vectors that are closer to each other(similar) for faces of the same person(looking similar). The above vectors now transform into:

We are not going to train such a network here as it takes a significant amount of data and computation power to train such networks. We will use a pre-trained network trained by Davis King on a dataset of ~3 million images. The network outputs a vector of 128 numbers which represent the most important features of a face.

Now that we know how this network works, let us see how we use this network on our own data. We pass all the images in our data to this pre-trained network to get the respective embeddings and save these embeddings in a file for the next step.

Comparing faces: Now that we have face embeddings for every face in our data saved in a file, the next step is to recognise a new t image that is not in our data. So the first step is to compute the face embedding for the image using the same network we used above and then compare this embedding with the rest of the embeddings we have. We recognise the face if the generated embedding is closer or similar to any other embedding as shown below:

So we passed two images, one of the images is of Vladimir Putin and other of George W. Bush. In our example above, we did not save the embeddings for Putin but we saved the embeddings of Bush. Thus when we compared the two new embeddings with the existing ones, the vector for Bush is closer to the other face embeddings of Bush whereas the face embeddings of Putin are not closer to any other embedding and thus the program cannot recognise him.

What is OpenCV

In the field of Artificial Intelligence, Computer Vision is one of the most interesting and Challenging tasks. Computer Vision acts like a bridge between Computer Software and visualizations around us. It allows computer software to understand and learn about the visualizations in the surroundings. For Example: Based on the color, shape and size determining the fruit. This task can be very easy for the human brain however in the Computer Vision pipeline, first we gather the data, then we perform the data processing activities and then we train and teach the model to understand how to distinguish between the fruits based on size, shape and color of fruit.

Currently, various packages are present to perform machine learning, deep learning and computer vision tasks. By far, computer vision is the best module for such complex activities. OpenCV is an open-source library. It is supported by various programming languages such as R, Python. It runs on most of the platforms such as Windows, Linux and MacOS.

To know more about how face recognition works on opencv, check out the free course on face recognition in opencv.

Advantages of OpenCV:

OpenCV is an open-source library and is free of cost.
As compared to other libraries, it is fast since it is written in C/C++.
It works better on System with lesser RAM
To supports most of the Operating Systems such as Windows, Linux and MacOS.

Installation:

Here we will be focusing on installing OpenCV for python only. We can install OpenCV using pip or conda(for anaconda environment).

Using pip:

Using pip, the installation process of openCV can be done by using the following command in the command prompt.

pip install opencv-python

Anaconda:

If you are using anaconda environment, either you can execute the above code in anaconda prompt or you can execute the following code in anaconda prompt.

conda install -c conda-forge opencv

Face Recognition using Python

In this section, we shall implement face recognition using OpenCV and Python. First, let us see the libraries we will need and how to install them:

OpenCV
dlib
Face_recognition

OpenCV is an image and video processing library and is used for image and video analysis, like facial detection, license plate reading, photo editing, advanced robotic vision, optical character recognition, and a whole lot more.

The dlib library, maintained by Davis King, contains our implementation of “deep metric learning” which is used to construct our face embeddings used for the actual recognition process.

The face_recognition library, created by Adam Geitgey, wraps around dlib’s facial recognition functionality, and this library is super easy to work with and we will be using this in our code. Remember to install dlib library first before you install face_recognition.

To install OpenCV, type in command prompt

pip install opencv-python

I have tried various ways to install dlib on Windows but the easiest of all of them is via Anaconda. First, install Anaconda (here is a guide to install it) and then use this command in your command prompt:

conda install -c conda-forge dlib

Next to install face_recognition, type in command prompt

pip install face_recognition

Now that we have all the dependencies installed, let us start coding. We will have to create three files, one will take our dataset and extract face embedding for each face using dlib. Next, we will save these embedding in a file.

In the next file we will compare the faces with the existing the recognise faces in images and next we will do the same but recognise faces in live webcam feed

Extracting features from Face

First, you need to get a dataset or even create one of you own. Just make sure to arrange all images in folders with each folder containing images of just one person.

Next, save the dataset in a folder the same as you are going to make the file. Now here is the code:

from imutils import paths

import face_recognition

import pickle

import cv2

import os

#get paths of each file in folder named Images

#Images here contains my data(folders of various persons)

imagePaths = list(paths.list_images('Images'))

knownEncodings = []

knownNames = []

# loop over the image paths

for (i, imagePath) in enumerate(imagePaths):

# extract the person name from the image path

name = imagePath.split(os.path.sep)[-2]

# load the input image and convert it from BGR (OpenCV ordering)

# to dlib ordering (RGB)

image = cv2.imread(imagePath)

rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

#Use Face_recognition to locate faces

boxes = face_recognition.face_locations(rgb,model='hog')

# compute the facial embedding for the face

encodings = face_recognition.face_encodings(rgb, boxes)

# loop over the encodings

for encoding in encodings:

knownEncodings.append(encoding)

knownNames.append(name)

#save emcodings along with their names in dictionary data

data = {"encodings": knownEncodings, "names": knownNames}

#use pickle to save data into a file for later use

f = open("face_enc", "wb")

f.write(pickle.dumps(data))

f.close()

Now that we have stored the embedding in a file named “face_enc”, we can use them to recognise faces in images or live video stream.

Face Recognition in Live webcam Feed

Here is the script to recognise faces on a live webcam feed:

import face_recognition

import imutils

import pickle

import time

import cv2

import os

#find path of xml file containing haarcascade file

cascPathface = os.path.dirname(

cv2.__file__) + "/data/haarcascade_frontalface_alt2.xml"

# load the harcaascade in the cascade classifier

faceCascade = cv2.CascadeClassifier(cascPathface)

# load the known faces and embeddings saved in last file

data = pickle.loads(open('face_enc', "rb").read())

print("Streaming started")

video_capture = cv2.VideoCapture(0)

# loop over frames from the video file stream

while True:

# grab the frame from the threaded video stream

ret, frame = video_capture.read()

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(gray,

scaleFactor=1.1,

minNeighbors=5,

minSize=(60, 60),

flags=cv2.CASCADE_SCALE_IMAGE)

# convert the input frame from BGR to RGB

rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# the facial embeddings for face in input

encodings = face_recognition.face_encodings(rgb)

names = []

# loop over the facial embeddings incase

# we have multiple embeddings for multiple fcaes

for encoding in encodings:

#Compare encodings with encodings in data["encodings"]

#Matches contain array with boolean values and True for the embeddings it matches closely

#and False for rest

matches = face_recognition.compare_faces(data["encodings"],

encoding)

#set name =inknown if no encoding matches

name = "Unknown"

# check to see if we have found a match

if True in matches:

#Find positions at which we get True and store them

matchedIdxs = [i for (i, b) in enumerate(matches) if b]

counts = {}

# loop over the matched indexes and maintain a count for

# each recognized face face

for i in matchedIdxs:

#Check the names at respective indexes we stored in matchedIdxs

name = data["names"][i]

#increase count for the name we got

counts[name] = counts.get(name, 0) + 1

#set name which has highest count

name = max(counts, key=counts.get)

# update the list of names

names.append(name)

# loop over the recognized faces

for ((x, y, w, h), name) in zip(faces, names):

# rescale the face coordinates

# draw the predicted face name on the image

cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(frame, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,

0.75, (0, 255, 0), 2)

cv2.imshow("Frame", frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

video_capture.release()

cv2.destroyAllWindows()

https://www.youtube.com/watch?v=fLnGdkZxRkg

Although in the example above we have used haar cascade to detect faces, you can also use face_recognition.face_locations to detect a face as we did in the previous script

Face Recognition in Images

The script for detecting and recognising faces in images is almost similar to what you saw above. Try it yourself and if you can’t take a look at the code below:

import face_recognition

import imutils

import pickle

import time

import cv2

import os

#find path of xml file containing haarcascade file

cascPathface = os.path.dirname(

cv2.__file__) + "/data/haarcascade_frontalface_alt2.xml"

# load the harcaascade in the cascade classifier

faceCascade = cv2.CascadeClassifier(cascPathface)

# load the known faces and embeddings saved in last file

data = pickle.loads(open('face_enc', "rb").read())

#Find path to the image you want to detect face and pass it here

image = cv2.imread(Path-to-img)

rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

#convert image to Greyscale for haarcascade

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(gray,

scaleFactor=1.1,

minNeighbors=5,

minSize=(60, 60),

flags=cv2.CASCADE_SCALE_IMAGE)

# the facial embeddings for face in input

encodings = face_recognition.face_encodings(rgb)

names = []

# loop over the facial embeddings incase

# we have multiple embeddings for multiple fcaes

for encoding in encodings:

#Compare encodings with encodings in data["encodings"]

#Matches contain array with boolean values and True for the embeddings it matches closely

#and False for rest

matches = face_recognition.compare_faces(data["encodings"],

encoding)

#set name =inknown if no encoding matches

name = "Unknown"

# check to see if we have found a match

if True in matches:

#Find positions at which we get True and store them

matchedIdxs = [i for (i, b) in enumerate(matches) if b]

counts = {}

# loop over the matched indexes and maintain a count for

# each recognized face face

for i in matchedIdxs:

#Check the names at respective indexes we stored in matchedIdxs

name = data["names"][i]

#increase count for the name we got

counts[name] = counts.get(name, 0) + 1

#set name which has highest count

name = max(counts, key=counts.get)

# update the list of names

names.append(name)

# loop over the recognized faces

for ((x, y, w, h), name) in zip(faces, names):

# rescale the face coordinates

# draw the predicted face name on the image

cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(image, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,

0.75, (0, 255, 0), 2)

cv2.imshow("Frame", image)

cv2.waitKey(0)

Output:

InputOutput

This brings us to the end of this article where we learned about face recognition.

You can also upskill with Great Learning’s PGP Artificial Intelligence and Machine Learning Course. The course offers mentorship from industry leaders, and you will also have the opportunity to work on real-time industry-relevant projects.

Original article source at: https://www.mygreatlearning.com

#python #opencv

Johathan McGlynn

1659283860

Activeinteraction: Manage Application Specific Business Logic Of Ruby

ActiveInteraction

ActiveInteraction manages application-specific business logic. It's an implementation of service objects designed to blend seamlessly into Rails.

ActiveInteraction gives you a place to put your business logic. It also helps you write safer code by validating that your inputs conform to your expectations. If ActiveModel deals with your nouns, then ActiveInteraction handles your verbs.

Installation
Basic usage
- Validations
Filters
- Basic Filters
- Advanced Filters
Rails
- Setup
- Controller
Advanced usage
Credits

API Documentation

Installation

Add it to your Gemfile:

gem 'active_interaction', '~> 5.1'

Or install it manually:

$ gem install active_interaction --version '~> 5.1'

This project uses Semantic Versioning. Check out GitHub releases for a detailed list of changes.

Basic usage

To define an interaction, create a subclass of ActiveInteraction::Base. Then you need to do two things:

Define your inputs. Use class filter methods to define what you expect your inputs to look like. For instance, if you need a boolean flag for pepperoni, use boolean :pepperoni. Check out the filters section for all the available options.

Define your business logic. Do this by implementing the #execute method. Each input you defined will be available as the type you specified. If any of the inputs are invalid, #execute won't be run. Filters are responsible for checking your inputs. Check out the validations section if you need more than that.

That covers the basics. Let's put it all together into a simple example that squares a number.

require 'active_interaction'

class Square < ActiveInteraction::Base
  float :x

  def execute
    x**2
  end
end

Call .run on your interaction to execute it. You must pass a single hash to .run. It will return an instance of your interaction. By convention, we call this an outcome. You can use the #valid? method to ask the outcome if it's valid. If it's invalid, take a look at its errors with #errors. In either case, the value returned from #execute will be stored in #result.

outcome = Square.run(x: 'two point one')
outcome.valid?
# => nil
outcome.errors.messages
# => {:x=>["is not a valid float"]}

outcome = Square.run(x: 2.1)
outcome.valid?
# => true
outcome.result
# => 4.41

You can also use .run! to execute interactions. It's like .run but more dangerous. It doesn't return an outcome. If the outcome would be invalid, it will instead raise an error. But if the outcome would be valid, it simply returns the result.

Square.run!(x: 'two point one')
# ActiveInteraction::InvalidInteractionError: X is not a valid float
Square.run!(x: 2.1)
# => 4.41

Validations

ActiveInteraction checks your inputs. Often you'll want more than that. For instance, you may want an input to be a string with at least one non-whitespace character. Instead of writing your own validation for that, you can use validations from ActiveModel.

These validations aren't provided by ActiveInteraction. They're from ActiveModel. You can also use any custom validations you wrote yourself in your interactions.

class SayHello < ActiveInteraction::Base
  string :name

  validates :name,
    presence: true

  def execute
    "Hello, #{name}!"
  end
end

When you run this interaction, two things will happen. First ActiveInteraction will check your inputs. Then ActiveModel will validate them. If both of those are happy, it will be executed.

SayHello.run!(name: nil)
# ActiveInteraction::InvalidInteractionError: Name is required

SayHello.run!(name: '')
# ActiveInteraction::InvalidInteractionError: Name can't be blank

SayHello.run!(name: 'Taylor')
# => "Hello, Taylor!"

Filters

You can define filters inside an interaction using the appropriate class method. Each method has the same signature:

Some symbolic names. These are the attributes to create.

An optional hash of options. Each filter supports at least these two options:

default is the fallback value to use if nil is given. To make a filter optional, set default: nil.

desc is a human-readable description of the input. This can be useful for generating documentation. For more information about this, read the descriptions section.

An optional block of sub-filters. Only array and hash filters support this. Other filters will ignore blocks when given to them.

Let's take a look at an example filter. It defines three inputs: x, y, and z. Those inputs are optional and they all share the same description ("an example filter").

array :x, :y, :z,
  default: nil,
  desc: 'an example filter' do
    # Some filters support sub-filters here.
  end

In general, filters accept values of the type they correspond to, plus a few alternatives that can be reasonably coerced. Typically the coercions come from Rails, so "1" can be interpreted as the boolean value true, the string "1", or the number 1.

Basic Filters

Array

In addition to accepting arrays, array inputs will convert ActiveRecord::Relations into arrays.

class ArrayInteraction < ActiveInteraction::Base
  array :toppings

  def execute
    toppings.size
  end
end

ArrayInteraction.run!(toppings: 'everything')
# ActiveInteraction::InvalidInteractionError: Toppings is not a valid array
ArrayInteraction.run!(toppings: [:cheese, 'pepperoni'])
# => 2

Use a block to constrain the types of elements an array can contain. Note that you can only have one filter inside an array block, and it must not have a name.

array :birthdays do
  date
end

For interface, object, and record filters, the name of the array filter will be singularized and used to determine the type of value passed. In the example below, the objects passed would need to be of type Cow.

array :cows do
  object
end

You can override this by passing the necessary information to the inner filter.

array :managers do
  object class: People
end

Errors that occur will be indexed based on the Rails configuration setting index_nested_attribute_errors. You can also manually override this setting with the :index_errors option. In this state is is possible to get multiple errors from a single filter.

class ArrayInteraction < ActiveInteraction::Base
  array :favorite_numbers, index_errors: true do
    integer
  end

  def execute
    favorite_numbers
  end
end

ArrayInteraction.run(favorite_numbers: [8, 'bazillion']).errors.details
=> {:"favorite_numbers[1]"=>[{:error=>:invalid_type, :type=>"array"}]}

With :index_errors set to false the error would have been:

{:favorite_numbers=>[{:error=>:invalid_type, :type=>"array"}]}

Boolean

Boolean filters convert the strings "1", "true", and "on" (case-insensitive) into true. They also convert "0", "false", and "off" into false. Blank strings will be treated as nil.

class BooleanInteraction < ActiveInteraction::Base
  boolean :kool_aid

  def execute
    'Oh yeah!' if kool_aid
  end
end

BooleanInteraction.run!(kool_aid: 1)
# ActiveInteraction::InvalidInteractionError: Kool aid is not a valid boolean
BooleanInteraction.run!(kool_aid: true)
# => "Oh yeah!"

File

File filters also accept TempFiles and anything that responds to #rewind. That means that you can pass the params from uploading files via forms in Rails.

class FileInteraction < ActiveInteraction::Base
  file :readme

  def execute
    readme.size
  end
end

FileInteraction.run!(readme: 'README.md')
# ActiveInteraction::InvalidInteractionError: Readme is not a valid file
FileInteraction.run!(readme: File.open('README.md'))
# => 21563

Hash

Hash filters accept hashes. The expected value types are given by passing a block and nesting other filters. You can have any number of filters inside a hash, including other hashes.

class HashInteraction < ActiveInteraction::Base
  hash :preferences do
    boolean :newsletter
    boolean :sweepstakes
  end

  def execute
    puts 'Thanks for joining the newsletter!' if preferences[:newsletter]
    puts 'Good luck in the sweepstakes!' if preferences[:sweepstakes]
  end
end

HashInteraction.run!(preferences: 'yes, no')
# ActiveInteraction::InvalidInteractionError: Preferences is not a valid hash
HashInteraction.run!(preferences: { newsletter: true, 'sweepstakes' => false })
# Thanks for joining the newsletter!
# => nil

Setting default hash values can be tricky. The default value has to be either nil or {}. Use nil to make the hash optional. Use {} if you want to set some defaults for values inside the hash.

hash :optional,
  default: nil
# => {:optional=>nil}

hash :with_defaults,
  default: {} do
    boolean :likes_cookies,
      default: true
  end
# => {:with_defaults=>{:likes_cookies=>true}}

By default, hashes remove any keys that aren't given as nested filters. To allow all hash keys, set strip: false. In general we don't recommend doing this, but it's sometimes necessary.

hash :stuff,
  strip: false

String

String filters define inputs that only accept strings.

class StringInteraction < ActiveInteraction::Base
  string :name

  def execute
    "Hello, #{name}!"
  end
end

StringInteraction.run!(name: 0xDEADBEEF)
# ActiveInteraction::InvalidInteractionError: Name is not a valid string
StringInteraction.run!(name: 'Taylor')
# => "Hello, Taylor!"

String filter strips leading and trailing whitespace by default. To disable it, set the strip option to false.

string :comment,
  strip: false

Symbol

Symbol filters define inputs that accept symbols. Strings will be converted into symbols.

class SymbolInteraction < ActiveInteraction::Base
  symbol :method

  def execute
    method.to_proc
  end
end

SymbolInteraction.run!(method: -> {})
# ActiveInteraction::InvalidInteractionError: Method is not a valid symbol
SymbolInteraction.run!(method: :object_id)
# => #<Proc:0x007fdc9ba94118>

Dates and times

Filters that work with dates and times behave similarly. By default, they all convert strings into their expected data types using .parse. Blank strings will be treated as nil. If you give the format option, they will instead convert strings using .strptime. Note that formats won't work with DateTime and Time filters if a time zone is set.

Date

class DateInteraction < ActiveInteraction::Base
  date :birthday

  def execute
    birthday + (18 * 365)
  end
end

DateInteraction.run!(birthday: 'yesterday')
# ActiveInteraction::InvalidInteractionError: Birthday is not a valid date
DateInteraction.run!(birthday: Date.new(1989, 9, 1))
# => #<Date: 2007-08-28 ((2454341j,0s,0n),+0s,2299161j)>

date :birthday,
  format: '%Y-%m-%d'

DateTime

class DateTimeInteraction < ActiveInteraction::Base
  date_time :now

  def execute
    now.iso8601
  end
end

DateTimeInteraction.run!(now: 'now')
# ActiveInteraction::InvalidInteractionError: Now is not a valid date time
DateTimeInteraction.run!(now: DateTime.now)
# => "2015-03-11T11:04:40-05:00"

date_time :start,
  format: '%Y-%m-%dT%H:%M:%S'

Time

In addition to converting strings with .parse (or .strptime), time filters convert numbers with .at.

class TimeInteraction < ActiveInteraction::Base
  time :epoch

  def execute
    Time.now - epoch
  end
end

TimeInteraction.run!(epoch: 'a long, long time ago')
# ActiveInteraction::InvalidInteractionError: Epoch is not a valid time
TimeInteraction.run!(epoch: Time.new(1970))
# => 1426068362.5136619

time :start,
  format: '%Y-%m-%dT%H:%M:%S'

Numbers

All numeric filters accept numeric input. They will also convert strings using the appropriate method from Kernel (like .Float). Blank strings will be treated as nil.

Decimal

class DecimalInteraction < ActiveInteraction::Base
  decimal :price

  def execute
    price * 1.0825
  end
end

DecimalInteraction.run!(price: 'one ninety-nine')
# ActiveInteraction::InvalidInteractionError: Price is not a valid decimal
DecimalInteraction.run!(price: BigDecimal(1.99, 2))
# => #<BigDecimal:7fe792a42028,'0.2165E1',18(45)>

To specify the number of significant digits, use the digits option.

decimal :dollars,
  digits: 2

Float

class FloatInteraction < ActiveInteraction::Base
  float :x

  def execute
    x**2
  end
end

FloatInteraction.run!(x: 'two point one')
# ActiveInteraction::InvalidInteractionError: X is not a valid float
FloatInteraction.run!(x: 2.1)
# => 4.41

Integer

class IntegerInteraction < ActiveInteraction::Base
  integer :limit

  def execute
    limit.downto(0).to_a
  end
end

IntegerInteraction.run!(limit: 'ten')
# ActiveInteraction::InvalidInteractionError: Limit is not a valid integer
IntegerInteraction.run!(limit: 10)
# => [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

When a String is passed into an integer input, the value will be coerced. A default base of 10 is used though it may be overridden with the base option. If a base of 0 is provided, the coercion will respect radix indicators present in the string.

class IntegerInteraction < ActiveInteraction::Base
  integer :limit1
  integer :limit2, base: 8
  integer :limit3, base: 0

  def execute
    [limit1, limit2, limit3]
  end
end

IntegerInteraction.run!(limit1: 71, limit2: 71, limit3: 71)
# => [71, 71, 71]
IntegerInteraction.run!(limit1: "071", limit2: "071", limit3: "0x71")
# => [71, 57, 113]
IntegerInteraction.run!(limit1: "08", limit2: "08", limit3: "08")
ActiveInteraction::InvalidInteractionError: Limit2 is not a valid integer, Limit3 is not a valid integer

Advanced Filters

Interface

Interface filters allow you to specify an interface that the passed value must meet in order to pass. The name of the interface is used to look for a constant inside the ancestor listing for the passed value. This allows for a variety of checks depending on what's passed. Class instances are checked for an included module or an inherited ancestor class. Classes are checked for an extended module or an inherited ancestor class. Modules are checked for an extended module.

class InterfaceInteraction < ActiveInteraction::Base
  interface :exception

  def execute
    exception
  end
end

InterfaceInteraction.run!(exception: Exception)
# ActiveInteraction::InvalidInteractionError: Exception is not a valid interface
InterfaceInteraction.run!(exception: NameError) # a subclass of Exception
# => NameError

You can use :from to specify a class or module. This would be the equivalent of what's above.

class InterfaceInteraction < ActiveInteraction::Base
  interface :error,
    from: Exception

  def execute
    error
  end
end

You can also create an anonymous interface on the fly by passing the methods option.

class InterfaceInteraction < ActiveInteraction::Base
  interface :serializer,
    methods: %i[dump load]

  def execute
    input = '{ "is_json" : true }'
    object = serializer.load(input)
    output = serializer.dump(object)

    output
  end
end

require 'json'

InterfaceInteraction.run!(serializer: Object.new)
# ActiveInteraction::InvalidInteractionError: Serializer is not a valid interface
InterfaceInteraction.run!(serializer: JSON)
# => "{\"is_json\":true}"

Object

Object filters allow you to require an instance of a particular class or one of its subclasses.

class Cow
  def moo
    'Moo!'
  end
end

class ObjectInteraction < ActiveInteraction::Base
  object :cow

  def execute
    cow.moo
  end
end

ObjectInteraction.run!(cow: Object.new)
# ActiveInteraction::InvalidInteractionError: Cow is not a valid object
ObjectInteraction.run!(cow: Cow.new)
# => "Moo!"

The class name is automatically determined by the filter name. If your filter name is different than your class name, use the class option. It can be either the class, a string, or a symbol.

object :dolly1,
  class: Sheep
object :dolly2,
  class: 'Sheep'
object :dolly3,
  class: :Sheep

If you have value objects or you would like to build one object from another, you can use the converter option. It is only called if the value provided is not an instance of the class or one of its subclasses. The converter option accepts a symbol that specifies a class method on the object class or a proc. Both will be passed the value and any errors thrown inside the converter will cause the value to be considered invalid. Any returned value that is not the correct class will also be treated as invalid. Any default that is not an instance of the class or subclass and is not nil will also be converted.

class ObjectInteraction < ActiveInteraction::Base
  object :ip_address,
    class: IPAddr,
    converter: :new

  def execute
    ip_address
  end
end

ObjectInteraction.run!(ip_address: '192.168.1.1')
# #<IPAddr: IPv4:192.168.1.1/255.255.255.255>

ObjectInteraction.run!(ip_address: 1)
# ActiveInteraction::InvalidInteractionError: Ip address is not a valid object

Record

Record filters allow you to require an instance of a particular class (or one of its subclasses) or a value that can be used to locate an instance of the object. If the value does not match, it will call find on the class of the record. This is particularly useful when working with ActiveRecord objects. Like an object filter, the class is derived from the name passed but can be specified with the class option. Any default that is not an instance of the class or subclass and is not nil will also be found. Blank strings passed in will be treated as nil.

class RecordInteraction < ActiveInteraction::Base
  record :encoding

  def execute
    encoding
  end
end

> RecordInteraction.run!(encoding: Encoding::US_ASCII)
=> #<Encoding:US-ASCII>

> RecordInteraction.run!(encoding: 'ascii')
=> #<Encoding:US-ASCII>

A different method can be specified by providing a symbol to the finder option.

Rails

ActiveInteraction plays nicely with Rails. You can use interactions to handle your business logic instead of models or controllers. To see how it all works, let's take a look at a complete example of a controller with the typical resourceful actions.

Setup

We recommend putting your interactions in app/interactions. It's also very helpful to group them by model. That way you can look in app/interactions/accounts for all the ways you can interact with accounts.

- app/
  - controllers/
    - accounts_controller.rb
  - interactions/
    - accounts/
      - create_account.rb
      - destroy_account.rb
      - find_account.rb
      - list_accounts.rb
      - update_account.rb
  - models/
    - account.rb
  - views/
    - account/
      - edit.html.erb
      - index.html.erb
      - new.html.erb
      - show.html.erb

Controller

Index

# GET /accounts
def index
  @accounts = ListAccounts.run!
end

Since we're not passing any inputs to ListAccounts, it makes sense to use .run! instead of .run. If it failed, that would mean we probably messed up writing the interaction.

class ListAccounts < ActiveInteraction::Base
  def execute
    Account.not_deleted.order(last_name: :asc, first_name: :asc)
  end
end

Show

Up next is the show action. For this one we'll define a helper method to handle raising the correct errors. We have to do this because calling .run! would raise an ActiveInteraction::InvalidInteractionError instead of an ActiveRecord::RecordNotFound. That means Rails would render a 500 instead of a 404.

# GET /accounts/:id
def show
  @account = find_account!
end

private

def find_account!
  outcome = FindAccount.run(params)

  if outcome.valid?
    outcome.result
  else
    fail ActiveRecord::RecordNotFound, outcome.errors.full_messages.to_sentence
  end
end

This probably looks a little different than you're used to. Rails commonly handles this with a before_filter that sets the @account instance variable. Why is all this interaction code better? Two reasons: One, you can reuse the FindAccount interaction in other places, like your API controller or a Resque task. And two, if you want to change how accounts are found, you only have to change one place.

Inside the interaction, we could use #find instead of #find_by_id. That way we wouldn't need the #find_account! helper method in the controller because the error would bubble all the way up. However, you should try to avoid raising errors from interactions. If you do, you'll have to deal with raised exceptions as well as the validity of the outcome.

class FindAccount < ActiveInteraction::Base
  integer :id

  def execute
    account = Account.not_deleted.find_by_id(id)

    if account
      account
    else
      errors.add(:id, 'does not exist')
    end
  end
end

Note that it's perfectly fine to add errors during execution. Not all errors have to come from checking or validation.

New

The new action will be a little different than the ones we've looked at so far. Instead of calling .run or .run!, it's going to initialize a new interaction. This is possible because interactions behave like ActiveModels.

# GET /accounts/new
def new
  @account = CreateAccount.new
end

Since interactions behave like ActiveModels, we can use ActiveModel validations with them. We'll use validations here to make sure that the first and last names are not blank. The validations section goes into more detail about this.

class CreateAccount < ActiveInteraction::Base
  string :first_name, :last_name

  validates :first_name, :last_name,
    presence: true

  def to_model
    Account.new
  end

  def execute
    account = Account.new(inputs)

    unless account.save
      errors.merge!(account.errors)
    end

    account
  end
end

We used a couple of advanced features here. The #to_model method helps determine the correct form to use in the view. Check out the section on forms for more about that. Inside #execute, we merge errors. This is a convenient way to move errors from one object to another. Read more about it in the errors section.

Create

The create action has a lot in common with the new action. Both of them use the CreateAccount interaction. And if creating the account fails, this action falls back to rendering the new action.

# POST /accounts
def create
  outcome = CreateAccount.run(params.fetch(:account, {}))

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:new)
  end
end

Note that we have to pass a hash to .run. Passing nil is an error.

Since we're using an interaction, we don't need strong parameters. The interaction will ignore any inputs that weren't defined by filters. So you can forget about params.require and params.permit because interactions handle that for you.

Destroy

The destroy action will reuse the #find_account! helper method we wrote earlier.

# DELETE /accounts/:id
def destroy
  DestroyAccount.run!(account: find_account!)
  redirect_to(accounts_url)
end

In this simple example, the destroy interaction doesn't do much. It's not clear that you gain anything by putting it in an interaction. But in the future, when you need to do more than account.destroy, you'll only have to update one spot.

class DestroyAccount < ActiveInteraction::Base
  object :account

  def execute
    account.destroy
  end
end

Edit

Just like the destroy action, editing uses the #find_account! helper. Then it creates a new interaction instance to use as a form object.

# GET /accounts/:id/edit
def edit
  account = find_account!
  @account = UpdateAccount.new(
    account: account,
    first_name: account.first_name,
    last_name: account.last_name)
end

The interaction that updates accounts is more complicated than the others. It requires an account to update, but the other inputs are optional. If they're missing, it'll ignore those attributes. If they're present, it'll update them.

class UpdateAccount < ActiveInteraction::Base
  object :account

  string :first_name, :last_name,
    default: nil

  validates :first_name,
    presence: true,
    unless: -> { first_name.nil? }
  validates :last_name,
    presence: true,
    unless: -> { last_name.nil? }

  def execute
    account.first_name = first_name if first_name.present?
    account.last_name = last_name if last_name.present?

    unless account.save
      errors.merge!(account.errors)
    end

    account
  end
end

Update

Hopefully you've gotten the hang of this by now. We'll use #find_account! to get the account. Then we'll build up the inputs for UpdateAccount. Then we'll run the interaction and either redirect to the updated account or back to the edit page.

# PUT /accounts/:id
def update
  inputs = { account: find_account! }.reverse_merge(params[:account])
  outcome = UpdateAccount.run(inputs)

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:edit)
  end
end

Advanced usage

Callbacks

ActiveSupport::Callbacks provides a powerful framework for defining callbacks. ActiveInteraction uses that framework to allow hooking into various parts of an interaction's lifecycle.

class Increment < ActiveInteraction::Base
  set_callback :filter, :before, -> { puts 'before filter' }

  integer :x

  set_callback :validate, :after, -> { puts 'after validate' }

  validates :x,
    numericality: { greater_than_or_equal_to: 0 }

  set_callback :execute, :around, lambda { |_interaction, block|
    puts '>>>'
    block.call
    puts '<<<'
  }

  def execute
    puts 'executing'
    x + 1
  end
end

Increment.run!(x: 1)
# before filter
# after validate
# >>>
# executing
# <<<
# => 2

In order, the available callbacks are filter, validate, and execute. You can set before, after, or around on any of them.

Composition

You can run interactions from within other interactions with #compose. If the interaction is successful, it'll return the result (just like if you had called it with .run!). If something went wrong, execution will halt immediately and the errors will be moved onto the caller.

class Add < ActiveInteraction::Base
  integer :x, :y

  def execute
    x + y
  end
end

class AddThree < ActiveInteraction::Base
  integer :x

  def execute
    compose(Add, x: x, y: 3)
  end
end

AddThree.run!(x: 5)
# => 8

To bring in filters from another interaction, use .import_filters. Combined with inputs, delegating to another interaction is a piece of cake.

class AddAndDouble < ActiveInteraction::Base
  import_filters Add

  def execute
    compose(Add, inputs) * 2
  end
end

Note that errors in composed interactions have a few tricky cases. See the errors section for more information about them.

Defaults

The default value for an input can take on many different forms. Setting the default to nil makes the input optional. Setting it to some value makes that the default value for that input. Setting it to a lambda will lazily set the default value for that input. That means the value will be computed when the interaction is run, as opposed to when it is defined.

Lambda defaults are evaluated in the context of the interaction, so you can use the values of other inputs in them.

# This input is optional.
time :a, default: nil
# This input defaults to `Time.at(123)`.
time :b, default: Time.at(123)
# This input lazily defaults to `Time.now`.
time :c, default: -> { Time.now }
# This input defaults to the value of `c` plus 10 seconds.
time :d, default: -> { c + 10 }

Descriptions

Use the desc option to provide human-readable descriptions of filters. You should prefer these to comments because they can be used to generate documentation. The interaction class has a .filters method that returns a hash of filters. Each filter has a #desc method that returns the description.

class Descriptive < ActiveInteraction::Base
  string :first_name,
    desc: 'your first name'
  string :last_name,
    desc: 'your last name'
end

Descriptive.filters.each do |name, filter|
  puts "#{name}: #{filter.desc}"
end
# first_name: your first name
# last_name: your last name

Errors

ActiveInteraction provides detailed errors for easier introspection and testing of errors. Detailed errors improve on regular errors by adding a symbol that represents the type of error that has occurred. Let's look at an example where an item is purchased using a credit card.

class BuyItem < ActiveInteraction::Base
  object :credit_card, :item
  hash :options do
    boolean :gift_wrapped
  end

  def execute
    order = credit_card.purchase(item)
    notify(credit_card.account)
    order
  end

  private def notify(account)
    # ...
  end
end

Having missing or invalid inputs causes the interaction to fail and return errors.

outcome = BuyItem.run(item: 'Thing', options: { gift_wrapped: 'yes' })
outcome.errors.messages
# => {:credit_card=>["is required"], :item=>["is not a valid object"], :"options.gift_wrapped"=>["is not a valid boolean"]}

Determining the type of error based on the string is difficult if not impossible. Calling #details instead of #messages on errors gives you the same list of errors with a testable label representing the error.

outcome.errors.details
# => {:credit_card=>[{:error=>:missing}], :item=>[{:error=>:invalid_type, :type=>"object"}], :"options.gift_wrapped"=>[{:error=>:invalid_type, :type=>"boolean"}]}

Detailed errors can also be manually added during the execute call by passing a symbol to #add instead of a string.

def execute
  errors.add(:monster, :no_passage)
end

ActiveInteraction also supports merging errors. This is useful if you want to delegate validation to some other object. For example, if you have an interaction that updates a record, you might want that record to validate itself. By using the #merge! helper on errors, you can do exactly that.

class UpdateThing < ActiveInteraction::Base
  object :thing

  def execute
    unless thing.save
      errors.merge!(thing.errors)
    end

    thing
  end
end

When a composed interaction fails, its errors are merged onto the caller. This generally produces good error messages, but there are a few cases to look out for.

class Inner < ActiveInteraction::Base
  boolean :x, :y
end

class Outer < ActiveInteraction::Base
  string :x
  boolean :z, default: nil

  def execute
    compose(Inner, x: x, y: z)
  end
end

outcome = Outer.run(x: 'yes')
outcome.errors.details
# => { :x    => [{ :error => :invalid_type, :type => "boolean" }],
#      :base => [{ :error => "Y is required" }] }
outcome.errors.full_messages.join(' and ')
# => "X is not a valid boolean and Y is required"

Since both interactions have an input called x, the inner error for that input is moved to the x error on the outer interaction. This results in a misleading error that claims the input x is not a valid boolean even though it's a string on the outer interaction.

Since only the inner interaction has an input called y, the inner error for that input is moved to the base error on the outer interaction. This results in a confusing error that claims the input y is required even though it's not present on the outer interaction.

Forms

The outcome returned by .run can be used in forms as though it were an ActiveModel object. You can also create a form object by calling .new on the interaction.

Given an application with an Account model we'll create a new Account using the CreateAccount interaction.

# GET /accounts/new
def new
  @account = CreateAccount.new
end

# POST /accounts
def create
  outcome = CreateAccount.run(params.fetch(:account, {}))

  if outcome.valid?
    redirect_to(outcome.result)
  else
    @account = outcome
    render(:new)
  end
end

The form used to create a new Account has slightly more information on the form_for call than you might expect.

<%= form_for @account, as: :account, url: accounts_path do |f| %>
  <%= f.text_field :first_name %>
  <%= f.text_field :last_name %>
  <%= f.submit 'Create' %>
<% end %>

This is necessary because we want the form to act like it is creating a new Account. Defining to_model on the CreateAccount interaction tells the form to treat our interaction like an Account.

class CreateAccount < ActiveInteraction::Base
  # ...

  def to_model
    Account.new
  end
end

Now our form_for call knows how to generate the correct URL and param name (i.e. params[:account]).

# app/views/accounts/new.html.erb
<%= form_for @account do |f| %>
  <%# ... %>
<% end %>

If you have an interaction that updates an Account, you can define to_model to return the object you're updating.

class UpdateAccount < ActiveInteraction::Base
  # ...

  object :account

  def to_model
    account
  end
end

ActiveInteraction also supports formtastic and simple_form. The filters used to define the inputs on your interaction will relay type information to these gems. As a result, form fields will automatically use the appropriate input type.

Shared input options

It can be convenient to apply the same options to a bunch of inputs. One common use case is making many inputs optional. Instead of setting default: nil on each one of them, you can use with_options to reduce duplication.

with_options default: nil do
  date :birthday
  string :name
  boolean :wants_cake
end

Optional inputs

Optional inputs can be defined by using the :default option as described in the filters section. Within the interaction, provided and default values are merged to create inputs. There are times where it is useful to know whether a value was passed to run or the result of a filter default. In particular, it is useful when nil is an acceptable value. For example, you may optionally track your users' birthdays. You can use the inputs.given? predicate to see if an input was even passed to run. With inputs.given? you can also check the input of a hash or array filter by passing a series of keys or indexes to check.

class UpdateUser < ActiveInteraction::Base
  object :user
  date :birthday,
    default: nil

  def execute
    user.birthday = birthday if inputs.given?(:birthday)
    errors.merge!(user.errors) unless user.save
    user
  end
end

Now you have a few options. If you don't want to update their birthday, leave it out of the hash. If you want to remove their birthday, set birthday: nil. And if you want to update it, pass in the new value as usual.

user = User.find(...)

# Don't update their birthday.
UpdateUser.run!(user: user)

# Remove their birthday.
UpdateUser.run!(user: user, birthday: nil)

# Update their birthday.
UpdateUser.run!(user: user, birthday: Date.new(2000, 1, 2))

Translations

ActiveInteraction is i18n aware out of the box! All you have to do is add translations to your project. In Rails, these typically go into config/locales. For example, let's say that for some reason you want to print everything out backwards. Simply add translations for ActiveInteraction to your hsilgne locale.

# config/locales/hsilgne.yml
hsilgne:
  active_interaction:
    types:
      array: yarra
      boolean: naeloob
      date: etad
      date_time: emit etad
      decimal: lamiced
      file: elif
      float: taolf
      hash: hsah
      integer: regetni
      interface: ecafretni
      object: tcejbo
      string: gnirts
      symbol: lobmys
      time: emit
    errors:
      messages:
        invalid: dilavni si
        invalid_type: '%{type} dilav a ton si'
        missing: deriuqer si

Then set your locale and run interactions like normal.

class I18nInteraction < ActiveInteraction::Base
  string :name
end

I18nInteraction.run(name: false).errors.messages[:name]
# => ["is not a valid string"]

I18n.locale = :hsilgne
I18nInteraction.run(name: false).errors.messages[:name]
# => ["gnirts dilav a ton si"]

Everything else works like an activerecord entry. For example, to rename an attribute you can use attributes.

Here we'll rename the num attribute on an interaction named product:

en:
  active_interaction:
    attributes:
      product:
        num: 'Number'

Credits

ActiveInteraction is brought to you by Aaron Lasseigne. Along with Aaron, Taylor Fausak helped create and maintain ActiveInteraction but has since moved on.

If you want to contribute to ActiveInteraction, please read our contribution guidelines. A complete list of contributors is available on GitHub.

ActiveInteraction is licensed under the MIT License.

Author: AaronLasseigne
Source code: https://github.com/AaronLasseigne/active_interaction
License: MIT license

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Activeinteraction: Manage Application Specific Business Logic Of Ruby

Installation

Basic usage

Validations

Filters

Basic Filters

Array

Boolean

File

Hash

String

Symbol

Dates and times

Numbers

Advanced Filters

Interface

Object

Record

Rails

Setup

Controller

Index

Show

New

Create

Destroy

Edit

Update

Advanced usage