In PCB boards you come across LEDs fixed in them, and due to the evolving world of technology, things are changing so first leading to the crafting of new technologies that have led to changes in the LED areas. The LED case is an area that has involved the combination of two methods of technologies in order to come up with something else that is more efficient when it comes to solving the intended purpose. This LED PCB comes with advantages of longevity and durability.
This is a type of PCB that is used for the purpose of lighting in the lighting appliances like modern LED bulbs. The material used in the process of making this board currently is the metal copper clad that has a very good heat-dissipating function. In general, we should note that the single layer LED PCB is made up of three layers namely the circuit layer, the ceramic layer and the insulation layer.
This is made up of the electrolytic copper foil and is etched to form the printed circuit board where the components are attached. When you do the comparison with the traditional boards, the LED board can carry a larger amount of current.
This layer is the core technology behind this type of boards and plays greater roles in the conduction insulation and bonding of the layers.
Here aluminum is the best choice compared to its availability and the cheap prices it offers. Stainless steel, silicon and the iron can be used if the thermal conductivity required is very high.
This consist of the substrate layer together with a conductive layer. A solder mask together with a silkscreen offer protection functions to these layers.
When you don a comparison with the single layer PCBs, you will realize that this has two copper layers which apparently makes them heavier as the number of conductive layers also increase to two.
There are two assembly methods that can be employed in the assembly if the LED PCBs. These methods are used to attach components on the board and we shall discuss it below;
This method involves mounting the electronic components directly into the board copper layer.
This method involves the drilling of holes into the PCB boards where components are then mounted to the holes using the long leads that are then soldered and the whole filled with flux.
This is one of the common applications the LED PCBs where they find great use on the consumer lighting from flash lights, lamps, spotlights, lanterns to solar powered lighting applications.
LED are also becoming a common application in the electronic devices such as the computer keyboards backlights. Other devices that have employed this technology are smartphones, tablets and the televisions.
Telecommunication displays and indicators use the LED PCBS because of their durability the ability to transfer heat and their longevity since telecommunication gadgets generate a lot of heat.
LEDs have a lot of use in the traffic and the transportation industry ranging from the stop lights and the automotive themselves. In the car this PCs are found in the headlights, fog lights, brake lights, reverse lights and the indicators. Highways tunnel lighting also use this technology. The modern streetlighting system is done using the LED PCBs.
Medical lighting and the medical equipment lighting that are used for medical examination and surgery often use this type LED PCBs.
This is the next lesson in our Python course. Previously, we looked at an overview of the different data types in python such as dictionaries, Boolean and sets. This tutorial will focus on Python sets to get a deeper understanding of this data type, so let's get started. During your schooling, there is a good chance you learned about sets and set theory. Venn diagrams may even be familiar to you:
Don't worry if you don't recognize this! You should still be able to access this tutorial without any problems. Rigidly defining a set in mathematics can be both abstract and difficult to understand. A set is thought of as a well-defined group of unique objects, which are sometimes called "elements."
Python's built-in set type facilitates the grouping of items into sets, which is important in programming as well. Unique actions that can be done on a set separate it from other object types.
Using Python, you learn how to create set objects and learn about the various activities they can be used for. We've covered lists and dictionaries in previous tutorials, so you should be familiar with when a set is the right tool for the job. You'll also look at "frozen sets," which are similar to sets but differ in one significant way.
The following features describe the built-in set type in Python:
Let us explore what all that entails, and how you can interact with sets in Python.
Iter> is an iterable (imagine a list or tuple for now) that generates a list of items to be included in the set. This is the same as the list method's iter> argument .extend():
A string can also be supplied to set() because strings are iterable. As you can see, list(s) generates a list of the characters in the string s. In the same way, set(s) generates a set of the characters in s:
The resulting sets are not in any order. The definition's original order isn't always followed. Values that are duplicated such as the string 'foo' in the first two examples and the letter 'u' in the third are only represented in the set once.
Curly braces () can also be used to define a set:
Each obj> becomes a separate element of the set when defined in this way, even if it is iterable. The .append() list technique works similarly. As a result, the sets depicted above can alternatively be described as follows:
To summarize:
Consider the following differences between these two definitions:
A set can be empty. The set() method is the sole way to define an empty set in Python because empty curly braces () are regarded as an empty dictionary.
In Boolean logic, an empty set is false:
A Boolean variable can only have two values in general: True or False. In other words, we call a variable a Boolean variable if it can only have these two values. It's frequently used to denote an expression's Truth value. True equals 1 and False equals 0 in mathematics. In contrast to electronics, a light bulb has a high value (that is 1) when it is switched on, and vice versa.
The len() function, which returns the number of items in a set, can be used to test for membership with the in and not in operators:
Sets are incompatible with many of the operations that operate with other composite python data types. Sets, for instance, cannot be indexed or sliced. Python, on the other hand, provides set object methods that are quite similar to the operations given for mathematical sets.
Most, but not all, set operations in Python can be accomplished using either an operator or a method. Let's look at how set union works as an illustration of how these operators and methods function. With sets, x1, and x2, the union of the two sets yields a set that contains all members from both sets.
Consider the following:
The results of combining x1 and x2 are shown below.
Note that in the union, 'baz,' will appear in both x1 and x2 only once. There are never any duplicate values in a set.
The | operator in Python can be used to execute set union:
The union() method can also be used to get a set union. The method is called using one of the sets as an input, and the other is supplied as a parameter:
The operator and method operate identically when used in the instances above. However, there is a distinction between them. Both operands must be set when using the | operator. In contrast, the union() method takes any iterable as an input, turns it into a set, and then executes the union.
Take note of the differences between the following two statements:
Both try to combine ('baz', 'qux', 'quux') with x1. The | operator fails, but the union() method succeeds.
A list of Python set operations is shown below. Some tasks are accomplished by an operator, while others are completed by a method, and still, others are completed by both. When a set is required, procedures normally accept any iterable as an input, whereas operators require actual sets as operands.
x1 | x2 [| x3 ...]
Add two or more sets together to get the unionset.
x1.union(x2) and x1 | x2: returns the sets of all items in either x1 or x2.
With either the operator or the method, you can specify more than two sets:
All elements that appear in any of the defined sets are included in the final set.
x1 & x2 [& x3 ...]
Calculate the point at where two or more sets intersect.
The set of items shared by both x1 and x2 is returned by x1.intersection(x2) and x1 & x2:
The intersection method and operator, like set union, allow you to specify multiple sets.
Only components that appear in all of the provided sets are included in the resulting set.
Calculate the difference between at least two sets.
Two examples of x1.difference are x1.difference(x2) and x1 - x2 (x2). produce a list of all x1 elements that aren't found in x2:
difference(x2) and x1 - x2 return the set that is returned when any elements in x2 are removed or subtracted from x1.
You can specify multiple sets once more:
The procedure is executed from left to right when several sets are supplied. In the foregoing example, the first step is to compute a - b, which yields 1, 2, 3, 300. After that, the set is taken from c, leaving 1, 2, and 3:
Calculate the difference between two symmetric sets.
The sets containing all items in x1 or x2, but not both, are returned by symmetric difference(x2) and x1 x2:
Additionally, the operator ^ enables for more than two sets:
The operation is executed from left to right once multiple sets are supplied, just like with the difference operator.
Surprisingly, although the operator supports multiple sets, the symmetric_difference() function does not:
Sets can be altered, even though their components need to be immutable types. Similar to the operations above, the contents of a set can be altered using a combination of operators and processes.
Each of the aforementioned operators has an augmented assignment form that can be used to change a set. Each person takes a different approach.
x1 |= x2 [| x3 ...]
The union can be used to change the state of a set.
x1 &= x2 [& x3 ...]
Intersection can be used to change a set.
x1 &= x2 and update(x2) x1 should be updated with only the items that present in both x1 and x2:
x1 -= x2 [| x3 ...]
Make a difference in a set.
x1.difference update(x2) and x1 -= x2 remove components found in x2 from x1:
x1 ^= x2
By using symmetric difference, you can change a set.
x1=x2 and update(x2) update x1, maintaining either x1 or x2 components, but not both:
Aside from the augmented operators listed above, Python has several other ways of modifying sets.
Adds a new element to a collection.
x.add(elem>) appends elem> to x:
Removes one of a set's elements.
elem> is removed from x using x.remove(elem>). If elem> is not in x, Python throws an exception:
Removes one of a set's elements.
elem> is also removed by x.discard(elem>). If elem> is not in x, this procedure does nothing instead of issuing an exception:
A set contains the random element to be removed from it.
x.pop() removes and returns an element from x that is picked at random. x.pop() throws an exception if x is null:
A frozenset is a Python in-built type that is similar to a set but is immutable. The following non-modifying procedures are possible on a frozenset:
Attempts to change a frozenset, on the other hand, fail:
You might suppose that because a frozenset is immutable, it can't be the target of an augmented assignment operator. However, keep the following in mind:
With frozensets in place, Python does not perform augmented assignments. The expression y &= s is practically the same as y = y & s. It makes no changes to the original x. It's associating x with a new item, and the one with which it was previously connected has vanished.
The id() method can be used to check this:
Following the augmented assignment, f has a new integer identification. It has been reassigned rather than changed in situ. When a Python object is the target of an augmented assignment operator, it is updated in place. Frozensets, on the other hand, are not. Frozensets are useful in cases where you need an immutable object yet wish to utilize a set. For example, because set elements must be immutable, a set with items that are also set cannot be defined.:
When you need to define a set of sets, frozensets, which are immutable, are the way to go:
Remember from the previous dictionary instruction that a dictionary key must be immutable. The in-built set type can't be used as a dictionary key for the following reason:
If you're looking for a way to use sets as dictionary keys, try frozensets:
We must use a membership operator to see if an element exists in a set. To check if an element is present in a sequence, membership operators are employed (e.g., strings, lists, tuples, sets, or dictionaries). As mentioned below, there are two membership operators.
Use the len () function to calculate the total number of items in a set. The number of items in an object is returned by this function. The function's input can be any sort of sequence, including a text, dictionary, list, or tuple, in addition to a set.
This tutorial teaches you how to create set objects in Python and how to interact with them using functions, operators, and methods. Python's main built-in data types should now be familiar to you. Then you'll examine the organization and structure of the code of a Python program that interacts with those items. In the next topic we will look at python list and python tuple.