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Introduction to Matlab trapz()MATLAB has incorporated the function trapz to process numerical integration using trapezoidal rule-following unit spacing. This rule is defined to approximate a numerical integration for a definite time frame by dividing the area under the plot into tine sized trapezoids. It helps in forming easily computable areas. The function returns the single return value of type scalar which is the final value of the integration.
Preferably trapz is recommended to use for numerical integration on discrete type data sets.
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SyntaxThis integrating function trapz accepts different input arguments to decide on the behavior of the integration function, being used with different syntaxes respectively.
They are:
Syntax Description
Q = trapz(Y) This syntax calculates the approximate integral value for ‘Y’ with unit spacing where the dimensions of the integration is determined based on the size of ‘Y’.
Q = trapz(X,Y) This syntax calculates the approximate integral value for ‘Y’ with ‘X’ specified scalar spacing, where the dimensions of the integration is determined based on the size of ‘Y’.
This syntax calculates the approximate integral value for the input with the dimensions of the integration given as ‘dim’.
The input arguments that contribute to different syntaxes of the function trapz can be explained as below:
Numeric Data-Y
The input data over which trapz function is called to operate numerical integration operation.
Depending on the nature of ‘Y’ the integration operation takes place differently such as:
1. Y as vector àThe output is an approximated integral result for ‘Y’
3. Y as multi-dimensional arrayà The operation takes place on the first non-unit sized dimension of ‘Y’ and results in the size of value 1 for that dimension while the size of the other dimensions remains unchanged.
The data type supported by ‘Y’ are:
single/ double/ complex number
Point spacing-X
The specified point spacing value over which the number of trapezoids to be formed is decided.
Value of X of different nature needs to follow come conditions as mentioned below:
1. X as vector à The length of the coordinate vector must be equal to the size of the first non-unit sized dimension of the numerical input ‘Y’.
2. X as scalaràIt satisfies the condition as
trapz(Y, X) = X*trapz(Y)
The default value of ‘X’ is ‘1’.
The data type supported by ‘X’ are single/ double
Dimensions for operation-dim
Decides the direction along which the integration operation will be performed.
2 different dimensions in which trapz works are as follows:
1. trapz(Y,1):
It operates on elements with columns of ‘Y’, returning a row vector as output.
2. trapz(Y,2):
It operates on elements with rows of ‘Y’, returning a column vector as output.
The value is dim must be a positive integer.
Examples of Matlab trapz()Here are the following examples mention below:
Example #1 – Numerical integration with Unit spacingThis operation can be executed by applying the syntax Q = trapz(Y) in the trapz function implementation in the MATLAB code.
Code:
The below code snippet is written to execute trapz operation on input numeric ‘Y’ as f(x)= 10*2^x.
Q = trapz(Y)
Output:
Example #2 – Numerical integration with Non-Unit spacingThis operation can be executed by applying the syntax Q = trapz(Y, X) in the trapz function implementation in the MATLAB code.
Code:
The below code is written to call trapz function on input numeric data ‘Y’ having point spacing value of pi/10.
Output:
Example #3 – Numerical integration with non-uniform spacingThis operation can be executed by applying the syntax Q = trapz(Y, X, dim) in the trapz function implementation in the MATLAB code.
Code:
The below code is written to call the trapz function on the input numeric data ‘Y’ with non-uniform point spacing values defined by ‘X’ with the value of ‘dim’ as ‘2’.
The point spacing is obtained by matrix ‘X’ which indicates that the trapezoids are formed non-uniformly.
The rows in input numeric data ‘Y’ is obtained from velocity data taken at 3 different trials.
Q = trapz(X,Y,2)
Output:
Trapz is executed on each row of the input numeric ‘Y’ as the value for ‘dim’ is set to 2. The resultant output is a column vector having an integration result for each row.
Example #4 – Multiple numerical integrationsThe integration function trapz can also be used to perform double integral i.e integration in the format:
In order to perform trapz function on double integral expression, the function is called in the nested loop as demonstrated in the below example:
I = trapz(q,trapz(p,F,2))
Output:
Example #5 – Real-time application of trapzThe trapz in MATLAB plays a vital role in integration calculations applied in real-time applications.
Code:
Trapz can be used to compute the average energy exhibited by an electrical machine for which v-i nonlinear relation is designed as:
The instantaneous voltage v(t)= sin(3t).
In the below code snippet the point spacing is defined by ‘time’ which is non-unit uniform spacing with the value of ‘10’.
The instantaneous power is calculated as p = f(i,v) = v*i.
The average energy (enrgy) is calculated by applying the trapz function.
xlabel(‘in seconds’); ylabel(‘in joules’)
Output:
Additional Note
The integration function trapz is similar to cumtrapz and difference lies as it reduces the dimension size to one and returns a single value without returning any intermediate values of integration.
If input numeric data ‘Y’ is multidimensional array by nature, it works across the first non-singleton dimension whereas the size of other dimensions remains unaffected.
It is similar to integral() and differs in the supported input data types. The function trapz works with discrete values whereas integral() works strictly with function (expression) integration operation.
Recommended ArticlesThis is a guide to Matlab trapz(). Here we discuss the different an introduction to Matlab trapz() with appropriate syntax and examples. You may also have a look at the following articles to learn more –
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A Quick Glance Of Mysql Having With Examples
Introduction to MySQL having
In real-time applications, we often need to generate complex reports from the data saved in the database and retrieve the filtered records to display the report to the user. For this, your SQL query must be optimized and correct so that the application’s performance is not hampered even if a large amount of data is present in the tables. The SELECT query constructed for such reports needs to use the functionalities provided in MySQL.
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Having a clause is one such functionality that helps apply the expressions’ filters. These expressions can consist of a single column, multiple columns, or even conditions applied to the grouped aggregated data retrieved using the GROUP BY clause. In this article, we will learn about the syntax of the HAVING clause, its evaluation order while execution, and study some examples to get a grip on the usage of the HAVING clause in your queries for retrieval of data.
Syntax:
The following SELECT query shows the syntax and placement of the HAVING clause in it –
SELECT list_of_expressions FROM name_of_table WHERE restrictions_and_conditions GROUP BY expressions_for_grouping HAVING condition_or_filter_on_grouped_expressions;In the above syntax –
list_of_expressions – The comma-separated list of the columns and the other expressions, such as aggregated values or manipulated values such as product, etc., that the query needs to retrieve from the table data.
name_of_table – The table’s name resides in your database and from where the summarized data needs to be retrieved for your use case.
restrictions_and_conditions – These are the conditions you can specify on the table columns that need to be fulfilled while retrieving the data.
expressions_for_grouping – The resultset retrieved from the query structure above the GROUP BY clause can be summarized and grouped based on certain expressions, including columns and aggregated values of columns as the expressions_for_grouping.
condition_or_filter_on_grouped_expressions – The WHERE clause only applies restrictions on the individual records or row of the column. We can use the HAVING clause to apply filters and restrictions and specify conditions on the grouped expressions of the resultset.
In the case of the HAVING clause, the restriction is applied to the grouped values retrieved instead of a single row of the table. When we do not use the GROUP BY clause in the SELECT query, the HAVING clause behaves in the same manner as that of the WHERE clause and applies restriction on individual row-level of the table as grouping expression is absent. If the filter condition specified in the HAVING clause evaluates to true, the grouped record is included in the final resultset based on the GROUP BY clause. Conversely, if the condition evaluates to false, the record is excluded.
Evaluation order:
The evaluation order followed by standard SQL is different than that of MySQL. Evaluation order determines which and when clause will be considered for execution when the SELECT query contains multiple clauses. The diagram below illustrates the order in which MySQL evaluates the clauses of the SELECT query.
Here, we observe that the evaluation of the HAVING clause takes place after the FROM, WHERE, SELECT, and GROUP BY clauses, but before the LIMIT and ORDER BY clauses. However, it is important to note that in the SQL standard, the evaluation order differs. In this case, the execution of the HAVING clause occurs after the GROUP BY clause and before the SELECT clause.
Example of MySQL having CREATE TABLE educba_articles ( id INTEGER AUTO_INCREMENT PRIMARY KEY, articlename varchar(10) NOT NULL, author varchar(10) NOT NULL, rate decimal(5,2) DEFAULT NULL, month varchar(10) NOT NULL, status varchar(10) NOT NULL, pages INTEGER DEFAULT NULL ) ENGINE=InnoDB DEFAULT CHARSET=latin1 COLLATE=latin1_danish_ci;that gives the following output after execution –
Now, we will insert some records in it –
INSERT INTO `educba_articles` VALUES (1, 'sp', 'Payal', 125.65, 'May','Submitted' ,3), (2,'having', 'Vyankatesh', 326.22, 'June','Pending' ,2), (3,'something', 'Omprakash', 123.22, 'July','Approved' ,2), (4, 'anything','sakshi', 645.54 ,'May','Pending' ,3), (5, 'everything','prerna', 356.54 ,'June','Submitted' ,4), (6,'events', 'Vyankatesh', 326.22, 'June','Approved' ,2), (7,'group by', 'Omprakash', 123.22, 'July','Approved' ,2), (8, 'from','sakshi', 645.54, 'May','Pending' ,3), (9, 'where','prerna', 356.54 ,'June','Submitted' ,4), (10, 'limit ', 'Payal', 125.65, 'May','Submitted' ,3), (11, 'coalesce ', 'Payal', 125.65, 'May','Submitted' ,3), (12,'order by', 'Vyankatesh', 326.22, 'June','Approved' ,2), (13,'datatypes', 'Omprakash', 123.22, 'July','Approved' ,2), (14, 'varchar','sakshi', 645.54 ,'May','Pending' ,3), (15, 'integer','prerna', 356.54 ,'June','Approved' ,4), (16,'date', 'Vyankatesh', 326.22, 'June','Pending' ,2), (17,'now', 'Omprakash', 123.22, 'July','Approved' ,2), (18, 'curdate','sakshi', 645.54 ,'May','Pending' ,3), (19, 'not null','prerna', 356.54, 'June','Pending' ,4);that provides the following result after execution –
SELECT author, MONTH, SUM(rate*pages) AS payment FROM educba_articles GROUP BY author, MONTHthat gives the following output –
Let’s consider another example where our goal is to retrieve the total payment that we need to pay to each author. In this case, the total payment for all months should exceed 5000. To achieve this, we need to group the data by author, calculate the payment by multiplying the rate and pages, and aggregate this value using the SUM() function to calculate the total payment for each author. Additionally, we should apply a restriction on the calculated value within the HAVING clause. Our query statement will look like follows –
SELECT author, SUM(rate*pages) AS payment FROM educba_articles GROUP BY author;that gives the following output after execution –
ConclusionHAVING clause can be used to apply restrictions and filters on the grouped expressions in complex queries that are generally used for reporting purposes using the SELECT query statement in MySQL.
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A Quick Glance On Opencv Version
Introduction to OpenCV Version
The OpenCV is the acronym of the open-source computer vision library used for virtual machine vision or eyesight. It is using in the computer or virtual machines for real-time applications. It is an open-source library used for video operating, image processing, and analysis vision applications. The OpenCV version is using to detect the object using image processing, save and display videos, and analyze and control the motion visions. It is the cross-platform library used for real-time vision and machine learning for robotics and the medical field. The OpenCV is an open-source library of images and videos for real-time computer applications. The users need to updated and adding new features to create the application effectively.
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Different OpenCV Versions 1. OpenCV Alpha Version
It was released in 2000.
It is the first version of the open-source library to create more effective images and videos.
The alpha versions are based on color changing, blending, and preserve the part of images using pixels.
The alpha version focuses on blending the two images without any disturbance of the image pixels.
The two images are placed in one place and create one image with setting color contrast.
The videos or images are collaborating and avoid overlapping of the images.
The latest alpha versions are OpenCV – 3.0.0 alpha and OpenCV – 4.0.0 alpha.
2. OpenCV Beta Version
It was released from 2001 to 2005.
The beta version focused on the contrast of the images and the brightness of the images and videos.
The different OpenCV beta versions were released between 2001 and 2005 year.
The OpenCV beta is mainly focused on the contrast of the images or video’s background and color co-ordinations.
The beta is set the brightness of the images and videos using pixels of the images.
The beta version is to operate and access the pixel values of the images and initialize with the matrix of the videos or images.
The latest beta versions are OpenCV – 3.0.0 beta and OpenCV – 4.0.0 beta.
3. OpenCV – 1.X Version
The OpenCV – 1.0 version released in the 2006.
The OpenCV – 1.0 is released in 2006 and OpenCV -1.1 version released in 2008.
It mainly focused on face detection and computational geometry using image processing.
The OpenCV – 1.0 version was used for motion templates for videos or images, SIFT features, and camera standardization.
It is focused on the Lucas – Kanade method for the movement of the image objects.
The OpenCV – 1.0 version is useful for decision-making trees, boosting methods of machine learning.
4. OpenCV – 2.X Version
The OpenCV-2.0 version released in the 2009.
The OpenCV 2.0 is based on the C++ library from 2009 – 2010.
The OpenCV – 2.3.0, OpenCV – 2.4.9, OpenCV – 2.4.10, OpenCV – 2.4.11, OpenCV – 2.4.12, and OpenCV – 2.4.13 are available for all browsers and operating system.
The OpenCV – 2.x version binding java and python automatically.
It is based on data structure, multidimensional array, linear and non-linear image filtering, and video analyst.
The OpenCV 2 version is used for geometrical image transformation like resize, color, space, etc.
The OpenCV 2 version focus on video motions modifying, background modification, and basic UI functions.
It was started with GPU acceleration for real-time applications in the computers.
It was started to set the functions easily in machine learning.
It is using for Java API and android manager for images and video operations.
5. OpenCV – 3.X Version
The OpenCV-3.0 version released in the 2023.
The OpenCV – 3.0.0, OpenCV – 3.1.0, OpenCV – 3.2.0, OpenCV – 3.3.0, OpenCV – 3.4.0, OpenCV – 3.4.12, and OpenCV – 3.4.13 are available for all browsers and operating system.
The OpenCV – 3.0.0 Alpha, OpenCV – 3.0.0 Beta, and OpenCV – 3.0.0 RC1 are available for all browsers and operating system.
It contains all basic functions with core, image processing, video input/output, image filtering, camera calibration, and video analysis.
It is used for machine learning with High-level GUI, video or image stabilization, and deep neural network module.
3.x version is focused on 3D image object recognition, improve foreground and background segmentation of the images, medical images, and vision method.
It was started with deep RGB image processing and text recognition or detection.
It extends the image processing and object detection features and modules.
It starts super 3D visualization and better image resolutions using image pixels.
6. OpenCV – 4.X Version
The OpenCV-4.0 version released in the 2023.
The OpenCV – 4.0.0, OpenCV – 4.1.0, OpenCV – 4.1.1, OpenCV – 4.1.2, OpenCV – 4.2.0, OpenCV – 4.3.0, OpenCV – 4.4.0 and OpenCV – 4.5.0 are available for all browsers and operating system.
The OpenCV – 4.0.0 Alpha, OpenCV – 4.0.0 Beta, and OpenCV – 4.0.0 RC1 are available for all browsers and operating systems.
The OpenCV 4.x is based on C++ 11 library and the OpenCV – 1.x version has been removed.
The OpenCV 4.x is the store and load the data from XML and JSON files.
The OpenCV 4.x is added to the graph-based image processing pipeline (G-API).
The OpenCV 4.x is focused on QR code detecting and decoding using the “objdetect” module.
The OpenCV 4.x is supporting to the features of deep learning.
Conclusion Recommended ArticlesThis is a guide to OpenCV Version. Here we discuss the introduction and different OpenCV versions for better understanding. You may also have a look at the following articles to learn more –
Attributes Of Matlab Pcolor() With Examples
Introduction to Matlab pcolor()
In MATLAB, pcolor() is the plotting function which is designed to represent the input matrix data in the form of an array of colored cells creating a pseudo color plot. The colored cell is termed as face. The plot gets created as x-y plane flat surface with x and y co-ordinates as vertices(corners) of faces. The limits for x and y co-ordinates are decided by the size of the input matrix.
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SyntaxThere are different syntaxes that can be used to implement pcolor() method based on the input arguments given to it. Such as:
Where C is the input matrix data
pcolor(C)
Where C is the input matrix data
pcolor(X,Y,C)
X– x-coordinates of the vertices
ax defines the axes targeted for the plot
To store the pseudo color plot as surface object
Attributes of Matlab pcolor()Here is the table of Attributes:
Attribute Description
C Input matrix data
X,Y coordinates of the vertices
Ax axes targeted for the plot
Examples to Implement Matlab pcolor()Here are the examples mentioned:
Example #1create pseudo color plot for input matric Cmat.
Code:
pcolor(Cmat);
Output:
Explanation: The input matrix is of size 2X3 and the vertices of the plot is decided 2X3 as default data. The pseudo color plot also can be created in the reverse direction using axis function.
Example #2Generate Hadamard square orthogonal matrix and create pseudo color plot for same,with the origin set in the upper left corner.
Code:
axis square
Output:
Explanation: The resultant graph is a 40X40 two-color map, represented in the reverse direction.
Example #3Create pseudo color plot for the input matrix data Cmat within the defined x and y coordinates given by matrix inputs X and Y.
Code:
pcolor(Xcod,Ycod,Cmat)
Output:
Explanation: The resultant plot has the vertices as per the values given by X and Y matrices and data from Cmat is plotted within the limits. The data from the input matrix are represented by different color code in the plot.
Example #4MATLAB supports tilling of plots using the functions like nexttile() and tiledlayout(). The below code is wriiten to create 1X2 chart layout and creating two different pseudo color plot for two different set of inputs and functions in each cell of the layout.
pcolor(Rx,RCmat)
Output:
Explantion: The resultant plots are arranged in single layout consisting of two tiles generated using the function nexttile().
Pcolor() with Surface ObjectWhen pcolor() method is assigned to a variable, it returns a surface object. This return value can be used to customize the properties of the plot after its creation.
Example #1The below code is developed create a pseudo color plot for input matrix and modify the edge color and line width after the plot is generated.
Code:
s.LineWidth = 5;
Output:
Explanation: The resultant pseudo color plot is assigned to variable ‘s’. The edge color and the line width of the plot is modified using ‘s’ value as shown in the output plot.
Example #2The below code snippet is designed to create the pseudo color plot and modify the face color of the cells using interpolated coloring using surface object properties.
Code:
s.FaceColor = ‘interp’;
Output:
Explanation: The resultant pseudo color plot is assigned to surface object ‘s’. The color representation of the faces are modified due to modification applied to the surface object property ‘facecolor’ for the surface object ‘s’.
Semi Logarithm Plotting using pcolor() Example #1The below MATLAB code is designed to create semi logarithmic pseudo color plot and to alter the appearance using surface object properties from its return value.
Code:
s = pcolor(X,YL,Cmat);
Output:
Explanation: The resultant output is surface plot where y-coordinate input is the logarithmic function of x-coordinate inputs. The pseudo color semi logarithmic plot generated from the pcolor() method can be stored as surface object and its properties can be altered as shown in the below example:
Example #2Code:
set(gca,’YTick’,logvals);
Output:
Explanation: The plot from the above code is generated with modified y-tick values by altering the properties of the surface object ‘s’.
Parametric Plotting using pcolor()Parametric functions are also supported by pcolor() method.
Example #1The below MATLAB code is designed to generate pseudo color plot from the x-y co oridinates generated from parametric equations.
Code:
pcolor(fX,fY,Cmat);
Explanation: The resultant plot represents the input matrix data, generated from repmat() function within the x-y coordinate values that are defined by two parametric equations.
Note: The size of the x-y coordinate grid must match the size of the input matrix data C. Based on the value in input matrix, the color map array gets colored from its vertices up to face chúng tôi first row gets mapped by the smallest value in C whereas the last row by the largest one, in the color map array. In case of long input array/matrix, the performance of pcolor() method seems to be slower.
Recommended ArticlesThis is a guide to Matlab pcolor(). Here we discuss an introduction to Matlab pcolor() with appropriate syntax and respective examples for better understanding. You can also go through our other related articles to learn more –
C++ Dynamic Allocation Of Arrays With Example
What is a Dynamic Array?
A dynamic array is quite similar to a regular array, but its size is modifiable during program runtime. DynamArray elements occupy a contiguous block of memory.
Once an array has been created, its size cannot be changed. However, a dynamic array is different. A dynamic array can expand its size even after it has been filled.
During the creation of an array, it is allocated a predetermined amount of memory. This is not the case with a dynamic array as it grows its memory size by a certain factor when there is a need.
In this C++ tutorial, you will learn
Factors impacting performance of Dynamic ArraysThe array’s initial size and its growth factor determine its performance. Note the following points:
If an array has a small size and a small growth factor, it will keep on reallocating memory more often. This will reduce the performance of the array.
If an array has a large size and a large growth factor, it will have a huge chunk of unused memory. Due to this, resize operations may take longer. This will reduce the performance of the array.
The new Keyword
In C++, we can create a dynamic array using the new keyword. The number of items to be allocated is specified within a pair of square brackets. The type name should precede this. The requested number of items will be allocated.
Syntax:The new keyword takes the following syntax:
pointer_variable = new data_type;The pointer_variable is the name of the pointer variable.
The data_type must be a valid C++ data type.
The keyword then returns a pointer to the first item. After creating the dynamic array, we can delete it using the delete keyword.
Example 1:using namespace std; int main() { int x, n; cout << “Enter the number of items:” << “n”; int *arr = new int(n); cout << “Enter ” << n << ” items” << endl; for (x = 0; x < n; x++) { } cout << “You entered: “; for (x = 0; x < n; x++) { cout << arr[x] << ” “; } return 0; }
Output:
Here is a screenshot of the code:
Code Explanation:
Include the iostream header file into our program to use its functions.
Include the std namespace in our program in order to use its classes without calling it.
Call the main() function. The program logic should be added within the body of the function.
Declare two integer variables x and n.
Print some text on the console prompting the user to enter the value of variable n.
Read user input from the keyboard and assigning it to variable n.
Declare an array to hold a total of n integers and assigning it to pointer variable *arr.
Print a message prompting the user to enter n number of items.
Use a for loop to create a loop variable x to iterate over the items entered by the user.
Read the elements entered by the user and storing them in the array arr.
End of the body of the for loop.
Print some text on the console.
Use a for loop to create a loop variable x to iterate over the items of the array.
Print out the values contained in the array named arr on the console.
End of the body of the for loop.
The program must return value upon successful completion.
End of the body of the main() function.
NOTE: In the above example, the user is allowed to specify any size for the array during run time. This means the array’s size is determined during runtime.
Initializing dynamically allocated arraysIt’s easy to initialize a dynamic array to 0.
Syntax:
int *array{ new int[length]{} };In the above syntax, the length denotes the number of elements to be added to the array. Since we need to initialize the array to 0, this should be left empty.
Example 2:using namespace std;
int main(void) {
int x;
int *array{ new int[5]{ 10, 7, 15, 3, 11 } };
cout << “Array elements: ” << endl;
for (x = 0; x < 5; x++) {
cout << array[x] << endl; }
return 0; }
Output:
Here is a screenshot of the code:
Code Explanation:
Include the iostream header file into our program to use its functions.
Include the std namespace in our program to use its classes without calling it.
Call the main() function. The program logic should be added within the body of the function.
Declare an integer variable named x.
Declare a dynamic array named array using an initializer list. The array will hold 5 integer elements. Note that we’ve not used the “=” operator between the array length and the initializer list.
Print some text on the console. The endl is a C++ keyword that means end line. It moves the cursor to the next sentence.
Use a for loop to iterate over the array elements.
Print the contents of the array named array on the console.
End of the body of the for loop.
The program must return value upon successful completion.
End of the body of the main() function.
Resizing ArraysThe length of a dynamic array is set during the allocation time.
However, C++ doesn’t have a built-in mechanism of resizing an array once it has been allocated.
You can, however, overcome this challenge by allocating a new array dynamically, copying over the elements, then erasing the old array.
Note: that this technique is prone to errors, hence, try to avoid it.
Dynamically Deleting ArraysA dynamic array should be deleted from the computer memory once its purpose is fulfilled. The delete statement can help you accomplish this. The released memory space can then be used to hold another set of data. However, even if you do not delete the dynamic array from the computer memory, it will be deleted automatically once the program terminates.
Note:
To delete a dynamic array from the computer memory, you should use delete[], instead of delete. The [] instructs the CPU to delete multiple variables rather than one variable. The use of delete instead of delete[] when dealing with a dynamic array may result in problems. Examples of such problems include memory leaks, data corruption, crashes, etc.
Example 3:using namespace std; int main() { int x, n; cout << “How many numbers will you type?” << “n”; int *arr = new int(n); cout << “Enter ” << n << ” numbers” << endl; for (x = 0; x < n; x++) { } cout << “You typed: “; for (x = 0; x < n; x++) { cout << arr[x] << ” “; } cout << endl; delete [] arr; return 0; }
Output:
Here is a screenshot of the code:
Code Explanation:
Include the iostream header file in our program in order to use its functions.
Include the std namespace in our program in order to use its classes without calling it.
Call the main() function. The program logic should be added within the body of the function.
Declare two variables x and n of the integer data type.
Print some text on the console. The text will ask the user to state the number of numbers they will enter.
Read user input from the keyboard. The input value will be assigned to variable n.
Declare a pointer variable *arr. The array arr will reserve some memory to store a total of n integers.
Print a message on the console prompting the user to enter n numbers.
Create a for loop and the loop variable x to iterate over the numbers entered by the user.
Read the numbers entered by the user and storing them in the array arr.
End of the body of the for loop.
Print some text on the console.
Use a for loop and the loop variable x to iterate over the contents of array arr.
Print out the values of the array arr on the console.
End of the body of the for loop.
Print an empty line on the console.
Free up the memory of the array arr.
The program will return value when it completes successfully.
End of the body of the main() function.
Summary:
Regular arrays have a fixed size. You cannot modify their size once declared.
With these types of arrays, the memory size is determined during compile time.
Dynamic arrays are different. Their sizes can be changed during runtime.
In dynamic arrays, the size is determined during runtime.
Dynamic arrays in C++ are declared using the new keyword.
We use square brackets to specify the number of items to be stored in the dynamic array.
Once done with the array, we can free up the memory using the delete operator.
Use the delete operator with [] to free the memory of all array elements.
A delete without [] frees the memory of only a single element.
There is no built-in mechanism to resize C++ arrays.
To initialize an array using a list initializer, we don’t use the “=” operator.
How To Implementgrid View With Example
Introduction to chúng tôi Grid View
Grid view is one of the very popular views in the current IT industry. This is one of the very common expectations currently by every client for presenting their screen specifically in Grid view. It is basically a table presentation based on the values that came from one data source. It can able to control that specific data source and display in the screen as per the requirement of the client, mainly each column considering as a field in the data source and each row considering as a record of that specific column. This control has various features for multiple types of presentation on the screen, we will cover it in this document in detail.
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Syntax of chúng tôi GridViewASP.net Gridview is one of the very popular implementations for the specific applications in the current scenario. Grid view mainly handles data sources, controlling the same, display it on the screen as a table. Here every column is mainly representing as a field, and each row in the data source represents a specific record of that corresponding column.
There have multiple features supports by this Grid view, explaining below:
SQLDataSource: Mainly require to maintain binding. Passing data source have huge data which mainly relate to specific fields in the screen. This control helps for binding those fields with data source available data.
Sorting: It has the ability to sorting the data. This Grid view presentation default gives one sorting utility, don’t need to write any additional code for that. Representing data can be sort as ascending or descending order based on the link provided in the screen.
Updating & Deleting: Can update or delete any data from the screen or data source. This ability ensures easy handle of the data from the screen by the end-user. This facility can be given based on some chosen parameters of the grid view presentation.
Pagination: This is also very much requiring features of any of the views presented in the current industry. When huge data came into the data source and Grid view unable to present entire data on the screen, it can default break it with multiple pages and giving this pagination utility to the end-user. It also has searching utility with any text, which helps the user to find out specific data easily.
Row selection: This is also one of the key utilities. Grid view gives the option to end-user for selecting one specific row, modify the required data, and save it. Modified data immediately display on the screen in the proper or expected view.
Key Fields: Grid view provided multiple key fields for presenting or handling big data from the data source.
Hyperlink: Grid view give good utility of handling multiple data source specifically on the hyperlink columns. Hyperlink columns have verities fields which developer can utilize as per their requirement.
Themes & Style: Grid view provides the ability of multiple appearances which can be easily handled by themes & style utilities have given by Grid view.
Example to Implement chúng tôi Grid ViewCreating a grid view presentation in chúng tôi application, developer normally needs to use below specific code:
Entire GridView presentation can be done by one behind the ASPX code for handling the dashboard and proper presentation.
Binding data in the GridView dashboard presentation with specific columns.
Edit corresponding data in the grid view.
Delete specific rows from the dashboard.
HTML code:
<asp:GridView runat=”server” AutoGenerateColumns=”False” OnRowEditing=”GridView1_RowEditing” OnRowUpdating=”GridView1_RowUpdating” OnRowCancelingEdit=”GridView1_RowCancelingEdit” <asp:RequiredFieldValidator runat=”server” ControlToValidate=”TextBox2″ <asp:RequiredFieldValidator runat=”server” ControlToValidate=”TextBox4″
Adding Page:
Binding:
public void BindMyGridview() { if (Session["myDatatable"] != null) { DataTable dt = (DataTable)Session["myDatatable"]; { GridView1.Visible = true; GridView1.DataSource = dt; GridView1.DataBind(); } else { GridView1.Visible = false; } } }Row Updating:
protected void GridView1_RowUpdating(object sender, GridViewUpdateEventArgs e) { TextBox TextBoxWithID = (TextBox)GridView1.Rows[e.RowIndex].FindControl("TextBox2"); TextBox TextBoxWithName = (TextBox)GridView1.Rows[e.RowIndex].FindControl("TextBox4"); string NewID = TextBoxWithID.Text.ToString(); string NewName = TextBoxWithName.Text.ToString(); DataTable dt = (DataTable)Session["myDatatable"]; DataRow dr = dt.Rows[e.RowIndex] dr["ID"] = NewID; dr["Name"] = NewName; dr.AcceptChanges(); Session["myDatatable"] = dt; GridView1.EditIndex = -1; BindMyGridview(); }Row Deleting:
protected void GridView1_RowDeleting(object sender, GridViewDeleteEventArgs e) { DataTable dt = (DataTable)Session["myDatatable"]; DataRow dr = dt.Rows[e.RowIndex]; dt.Rows.Remove(dr); GridView1.EditIndex = -1; BindMyGridview(); }Select DOT NET Framework 3.5 from the drop-down.
Open the page design page, where all the attribute needs to be designed.
Take one new form for proper design. Inside the form, the table has been designed. ID and Name are there where specific data table data should display.
Choose a specific online template for writing the background logic of the Grid View presentation.
Preparing the code for the application and mapping the corresponding field for further execution.
Choosing specific data sources for using and presenting Grid view data as per requirement.
Designing the dashboard also been executing in the preview page, displaying require information.
Writing the ASPX code for preparing the view presentation. It automatically came based on the page design.
Working with a text box to define the field name, catch with the value, and perform the required tasks.
Set one specific dashboard page as a starting page for running the application.
Add multiple data for generating a proper grid view presentation.
Add another data for the proper grid view dashboard presentation.
Adding one more data for displaying more in the dashboard.
Displaying the main dashboard of grid view, where all the added data properly displayed on the screen including edit and delete link.
After the update, this page again returns back to the dashboard with a proper grid view.
The delete button is there for deleting one specific record from the grid view presentation.
ConclusionASP.net Grid view presentation is one of the common and key requirements from any of the clients in the current scenario. This presentation or designing the page is comparatively easy to do for the developer rather than performing normal design. The page has multiple fields to handle and present. Every field is actually bound with corresponding data coming from the backend code.
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